RELATIONSHIP BETWEEN HOPELESSNESS AND NON-SUICIDAL SELF-INJURY IN ADOLESCENTS: A CROSS-SECTIONAL STUDY IN JOMBANG, INDONESIA
Dessy Ekawati1*, Agustina Maunaturrohmah1, Anin Wijayanti1, Ifa Nofalia2
- Professional Nursing Program, Faculty of Health Sciences, Institut Teknologi Sains dan Kesehatan Insan Cendekia Medika, Jombang, Indonesia.
- Bachelor Nursing Program, Faculty of Health Sciences, Institut Teknologi Sains dan Kesehatan Insan Cendekia Medika, Jombang, Indonesia.
* Corresponding author: Dessy Ekawati., Professional Nursing Program, Faculty of Health Sciences, Institut Teknologi Sains dan Kesehatan Insan Cendekia Medika, Jombang, Indonesia.
E-mail: dessyekawati.s1201@gmail.com
Cite this article
ABSTRACT
Introduction: Non-Suicidal Self-Injury (NSSI) has become an increasing mental health concern among adolescents and is strongly associated with negative cognitive–emotional states, particularly hopelessness. Adolescents experiencing hopelessness are more vulnerable to engaging in maladaptive coping behaviors, including self-injury.
Objective: This study aimed to examine the relationship between hopelessness and NSSI and to determine the role of hopelessness as a predictor of self-injurious behavior among adolescents.
Materials and Methods: This study employed a quantitative cross-sectional design involving 138 senior high school students aged 15–19 years selected using stratified random sampling. Hopelessness was measured using the Beck Hopelessness Scale (BHS), while Non-Suicidal Self-Injury (NSSI) was assessed using the Inventory of Statements About Self-Injury (ISAS). Data were analyzed using Spearman’s rank correlation test to determine the relationship between variables.
Results: The findings revealed that most respondents experienced moderate levels of hopelessness (40.6%) and mild levels of NSSI (59.4%). Statistical analysis showed a significant very strong positive correlation between hopelessness and NSSI (r = 0.876; p-value < 0.001), indicating that higher levels of hopelessness were associated with increased frequency and severity of self-injurious behavior.
Conclusion: The results indicate that hopelessness plays a significant role in the development of NSSI among adolescents. Adolescents with higher levels of hopelessness tend to have poorer emotional regulation and are more likely to engage in self-injury as a coping mechanism. These findings highlight the importance of early identification of hopelessness in adolescents and the need for targeted mental health nursing interventions to reduce the risk of Non-Suicidal Self-Injury (NSSI).
Keywords: Adolescents; Hopelessness; NSSI; Mental health; Self-injury
INTRODUCTION
Non-Suicidal Self-Injury (NSSI) has emerged as one of the most concerning mental health problems among adolescents. This behavior is no longer viewed as a transient or incidental phenomenon, but rather as a significant psychological response to prolonged emotional distress and internal conflict [1]. Adolescence is a developmental period characterized by identity formation, emotional instability, and interpersonal challenges, which increases vulnerability to maladaptive coping strategies such as self-injury. Non-Suicidal Self-Injury (NSSI) is often used as a means to regulate overwhelming emotions, reduce psychological pain, or cope with feelings of emptiness and helplessness. Clinically, NSSI manifests through intentional tissue damage such as cutting, burning, or scratching without suicidal intent. This characteristic distinguishes it from suicidal self-injury where the primary goal is to end one’s life. Unlike suicidal behavior, NSSI often serves as a maladaptive affect regulation strategy to relieve intense psychological tension. If left unaddressed, this behavior may persist and increase the risk of more severe mental health problems, including suicidal behavior [2].
Globally, the prevalence of Non-Suicidal Self-Injury (NSSI) among adolescents ranges from 10% to 35%, with higher rates reported in individuals aged 15–19 years. Studies conducted in Europe and North America indicate that approximately 17–24% of adolescents have engaged in at least one form of self-injurious behavior, while in several Asian countries the prevalence exceeds 30% [3]. In Indonesia, national data indicate that emotional and mental health problems among adolescents aged 15–24 years have increased significantly, reaching more than 20%, accompanied by a growing trend of self-harm behavior and suicidal ideation [4]. These findings highlight the urgency of addressing Non-Suicidal Self-Injury (NSSI) as a major public health concern.
Various psychological factors have been associated with Non-Suicidal Self-Injury (NSSI), including depression, anxiety, emotional dysregulation, trauma, and cognitive distortions. Among these, hopelessness is considered a central cognitive–affective factor [5]. Hopelessness reflects negative expectations about the future, a loss of meaning in life, and the belief that current difficulties will not improve. In adolescents, this condition may arise from academic stress, family conflict, bullying, and social rejection [6]. Previous studies have shown that hopelessness is strongly associated with depressive symptoms, increased risk of self-injury, and progression toward suicidal ideation. It also contributes to impaired social functioning, decreased academic performance, and withdrawal from social interactions [7].
From a nursing perspective, hopelessness represents a critical psychosocial problem that affects an individual’s motivation, coping ability, and overall well-being. Nurses play a pivotal role in early detection, yet many professionals, particularly in school settings, report challenges in distinguishing subtle signs of NSSI and hopelessness due to a lack of specialized psychiatric training. In the Indonesian context, the presence of dedicated school nurses to assess and manage student mental health is still limited, with responsibilities often falling to general health teachers or school counselors. Nursing interventions that focus on enhancing hope, restructuring negative cognitions, and strengthening adaptive coping strategies are essential in preventing Non-Suicidal Self-Injury (NSSI). However, effective intervention strategies require strong empirical evidence regarding the role of hopelessness and its association with self-injurious behavior among adolescents [8].
Objective
This study aims to examine the relationship between hopelessness and Non-Suicidal Self-Injury (NSSI) among adolescents and to analyze the strength and direction of the association between these two variables using a cross-sectional approach.
MATERIALS AND METHODS
Study Population
This research employed a cross-sectional study design to investigate the relationship between hopelessness and Non-Suicidal Self-Injury (NSSI) behavior. The study was conducted across three selected Senior High Schools in Jombang Regency, East Java, Indonesia, during the period of February to March 2026. A total of 138 adolescents were recruited as participants through a stratified random sampling technique, with strata defined by grade levels (Grades X, XI, and XII) across the selected schools. The sample size was determined using the G*Power 3.1.9.7 software for a correlation bivariate model with an effect size of 0.3 which represents a medium effect, an alpha level of 0.05, and a power of 0.95. These parameters yielded a minimum required sample of 134 participants, therefore the 138 participants included in this study provided sufficient statistical power. The research protocol was strictly guided by the ethical principles of the Declaration of Helsinki and received formal approval from the Health Research Ethics Committee of the Faculty of Health, Institut Teknologi Sains dan Kesehatan (ITSKes) Insan Cendekia Medika Jombang on January 12, 2026, with the issuance of protocol number KEPK/ICME/031/I/2026.
Inclusion criteria
The participants eligible for this study were limited to adolescents aged 15–19 years who were actively enrolled as students in the participating schools at the time of data collection. Inclusion also required a demonstrated willingness to participate in the research, which was confirmed by the submission of signed informed consent forms. Furthermore, participants were required to be capable of understanding and completing the research questionnaires independently without external assistance. For participants under 18 years old, additional written consent was obtained from their parents or legal guardians.
Exclusion criteria
The study excluded adolescents who had a documented medical history of severe psychiatric disorders, such as schizophrenia or bipolar disorder, as well as those currently undergoing intensive psychological or psychiatric therapy. To ascertain these exclusion criteria, the research team conducted a two-step verification process. This involved reviewing students' confidential health records in collaboration with school counselors and performing brief clinical screening interviews prior to enrollment to identify any overt signs of psychotic symptoms or cognitive impairment. Additionally, students who withdrew their participation at any point during the data collection process or those who provided incomplete responses to the instruments were also excluded from the final analysis.
Data Collection Procedure
Data collection was carried out in a designated quiet room within each school to ensure participant privacy and confidentiality. The researchers first explained the study purpose and the voluntary nature of participation. Once consent was secured, participants completed a sociodemographic questionnaire followed by the BHS and ISAS scales. The sociodemographic questionnaire included items regarding age, gender, grade level, living arrangements, and a specific self-report question asking whether they had ever experienced significant emotional problems in the past. The entire process took approximately 20-30 minutes per student. To ensure language comprehension, a researcher was present throughout the session to clarify any confusing terminology even though the instruments had already been pilot-tested for clarity.
Instruments
Data were collected using two primary instruments that underwent a rigorous forward-back translation process into the Indonesian language to maintain linguistic and cultural equivalence. The instruments used in this study are widely recognized in the public domain for academic and research purposes. Hopelessness was measured using the Beck Hopelessness Scale (BHS) developed by Beck et al. (1974) consisting of 20 true or false items that evaluate negative expectations about the future. In this study, the Indonesian version of the BHS demonstrated high internal consistency with a Cronbach’s alpha of 0.88. Non-Suicidal Self-Injury (NSSI) was assessed using the Inventory of Statements about Self-Injury (ISAS) adapted from Nock et al. (2010) which measures the frequency and psychological functions of self-injurious behaviors. The Indonesian adaptation of the ISAS was specifically validated for this study and yielded a Cronbach’s alpha of 0.84. Prior to the main data collection, a pilot study was conducted with 30 adolescents in a similar demographic area to ensure the terminology was easily understood by the target population. This section also included the collection of sociodemographic variables such as age, gender, and family structure which are subsequently reported in the results.
Statistical analysis
The collected data were processed and analyzed using IBM SPSS Statistics version 26.0. Descriptive statistics were utilized to summarize the demographic characteristics and profiles of the participants. To determine the strength and direction of the association between hopelessness and Non-Suicidal Self-Injury (NSSI), the Spearman rho rank correlation test was performed utilizing a two-tailed test. This choice was justified considering that the data were not normally distributed, as confirmed by the Kolmogorov-Smirnov normality test, and the variables were ordinal in nature. For all statistical tests in this study, a p-value < 0.05 was considered statistically significant.
RESULTS
Sample Characteristics
Based on Table 1, the characteristics of the respondents show that out of 138 students, the largest age group was 16 years old with 42 students (30.4%), followed by those aged 17 years with 39 students (28.3%), 15 years with 28 students (20.3%), 18 years with 21 students (15.2%), and 19 years with 8 students (5.8%). The mean age of the respondents was 16.7 years with a standard deviation of 1.02. In terms of gender, female students predominated, accounting for 77 respondents (55.8%), while male students numbered 61 (44.2%).
Regarding grade level, most participants were in Grade XI with 49 students (35.5%), followed by Grade X with 46 students (33.3%) and Grade XII with 43 students (31.2%).
Concerning living status, the majority of respondents lived with their parents (102 students; 73.9%), whereas 21 students (15.2%) lived with relatives and 15 students (10.9%) resided in a boarding school or dormitory.
With respect to the history of emotional problems, 80 respondents (58.0%) reported having experienced emotional problems, while 58 respondents (42.0%) reported none.
Characteristics N % Mean SD Age (years) 15 years 28 20.3 16.7 1.02 16 years 42 30.4 17 years 39 28.3 18 years 21 15.2 19 years 8 5.8 Gender Male 61 44.2 Female 77 55.8 Grade Grade X 46 33.3 Grade XI 49 35.5 Grade XII 43 31.2 Living Status With parents 102 73.9 With relatives 21 15.2 Boarding school/dormitory 15 10.9 History of Emotional Problems Yes 80 58.0 No 58 42.0 Table 1. Sociodemographic Characteristics of Respondents (N = 138).
Descriptive Analysis of Hopelessness and Non-Suicidal Self-Injury (NSSI)
The distribution of hopelessness levels shows that the majority of respondents fell into the moderate category (N = 68; 49.3%), followed by mild and high levels (N = 24 for each; 17.4%), while the remaining students reported low levels (N = 22; 15.9%).
Regarding Non-Suicidal Self-Injury (NSSI), nearly half of the participants were categorized as having a moderate frequency (N = 66; 47.8%), followed by low frequency (N = 41; 29.7%), no Non-Suicidal Self-Injury (NSSI) (N = 18; 13.0%), and high frequency (N = 13; 9.4%).
Characteristics N % Mean SD Hopelessness Level Low 22 15.9 2.68 0.944 Mild 24 17.4 Moderate 68 49.3 High 24 17.4 Non-Suicidal Self-Injury (NSSI) Category No NSSI 18 13.0 2.54 0.838 Low frequency 41 29.7 Moderate frequency 66 47.8 High frequency 13 9.4 Table 2. Distribution of Hopelessness Levels and NSSI Categories (N = 138).
Correlation Between Hopelessness and Non-Suicidal Self-Injury Among Adolescents
To determine the relationship between hopelessness and Non-Suicidal Self-Injury (NSSI), a Spearman rank correlation test was performed. As shown in Table 3, the results indicate a positive and statistically significant relationship between the two variables, with a correlation coefficient of r = 0.876 and a p-value < 0.001. This very strong correlation suggests that as the level of hopelessness increases, the frequency of Non-Suicidal Self-Injury (NSSI) behaviors among adolescents also significantly increases.
Variable M (SD) Median (IQR) Sig. (2-tailed) Spearman Correlation Hopelessness 10.27 (5.05) 11 [6.00, 14.00] Correlation coefficient r = 0.876, p < 0.001* NSSI 10.24 (7.64) 11 [3.75, 15.00] Note: *=significant test, SD = standard deviation, IQR = interquartile range [Q1, Q3].
Table 3. Spearman Correlation Analysis of Hopelessness and NSSI (N = 138)
Figure 1 presents a scatter plot illustrating the correlation between hopelessness scores and Non-Suicidal Self-Injury (NSSI) frequency among adolescents. The visual distribution of data points reveals a consistent upward linear pattern, where an increase in hopelessness scores is accompanied by a rise in Non-Suicidal Self-Injury (NSSI) scores. The analysis confirms a very strong positive correlation between these two variables (r = 0.876, p-value < 0.001), indicating that higher levels of hopelessness are significantly associated with higher levels of Non-Suicidal Self-Injury (NSSI) behavior. This strong linear relationship underscores the critical role of hopelessness as a psychological factor in self-injurious actions.
Figure 1. Scatter plot showing the correlation between hopelessness and NSSI.
The crosstabulation analysis further illustrates the distribution of respondents based on hopelessness levels and Non-Suicidal Self-Injury (NSSI) categories. Among respondents with moderate levels of hopelessness, the majority were categorized as having moderate frequency Non-Suicidal Self-Injury (NSSI) (55 students), while 13 students were in the low frequency category, totaling 68 individuals. In contrast, respondents with severe hopelessness were mostly distributed in the moderate frequency (11 students) and high frequency Non-Suicidal Self-Injury (NSSI) categories (13 students), totaling 24 individuals.
For those with mild hopelessness, all respondents were categorized in the low frequency Non-Suicidal Self-Injury (NSSI) group (24 students). Meanwhile, among respondents with low hopelessness, most reported no NSSI behavior (18 students), while a smaller proportion fell into the low frequency category (4 students), totaling 22 individuals (Table 4).
Hopelessness Non-Suicidal Self-Injury (NSSI) Total No Low frequency Moderate frequency High frequency Low 18 4 0 0 22 Mild 0 24 0 0 24 Moderate 0 13 55 0 68 High 0 0 11 13 24 Total 18 41 66 13 138 Table 4. Crosstabulation of Hopelessness and Non-Suicidal Self-Injury.
DISCUSSION
Based on the respondents’ characteristics, the largest age group was 16 years old (42 students; 30.4%), with a mean age of 16.7 ± 1.02 years, representing the middle-adolescent developmental stage. According to Erikson’s psychosocial theory, this stage corresponds to the phase of identity versus role confusion, in which adolescents are highly sensitive to academic pressure, peer relationships, and identity exploration, making them more vulnerable to emotional distress and feelings of hopelessness [9].
The predominance of female students (77 students; 55.8%) is consistent with previous findings indicating that adolescent girls are more likely to experience internalizing problems, including hopelessness and self-injurious behavior, than boys [1,2]. Most respondents were in Grade XI (49 students; 35.5%), a period characterized by increasing academic demands and future-related concerns, which, according to developmental stress theory, may intensify psychological strain.
The majority of participants lived with their parents (102 students; 73.9%), suggesting that hopelessness and self-injury can occur not only among adolescents separated from their families but also within intact family settings, depending on the quality of emotional support. Furthermore, more than half of the respondents reported a history of emotional problems (80 students; 58.0%), supporting the diathesis–stress model, which posits that pre-existing psychological vulnerability increases the risk of maladaptive emotional responses under stress [5].
The distribution of hopelessness levels showed that the moderate category was the most prevalent (68 students; 49.3%), with a mean score of 10.27 ± 5.05. This finding indicates that a substantial proportion of adolescents experienced pessimistic expectations about the future and a reduced sense of control over life outcomes. According to Beck’s cognitive theory, hopelessness arises from negative cognitive schemas and distorted beliefs about the self, the world, and the future, which are particularly salient during adolescence when individuals face academic, interpersonal, and identity-related challenges [10,11]. Similar patterns have been reported, showing that moderate levels of hopelessness are common in adolescents and constitute an important risk factor for emotional and behavioral problems, including self-injury [12,13]. From a psychiatric nursing perspective, these moderate levels of hopelessness necessitate early identification and cognitive interventions to prevent the development of more severe psychological crises.
Regarding Non-Suicidal Self-Injury (NSSI), the largest proportion of respondents fell into the moderate frequency category (66 students; 47.8%), followed by low frequency (41 students; 29.7%). A smaller proportion reported no NSSI behavior (18 students; 13.0%), while only 13 students (9.4%) were categorized as having high frequency NSSI. The mean NSSI score was 10.24 ± 7.64. This finding suggests that many adolescents engage in self-injurious behavior at a moderate level, which may reflect repeated use of Non-Suicidal Self-Injury (NSSI) as a coping mechanism rather than isolated incidents. In line with Nock’s (2010) functional model of Non-Suicidal Self-Injury (NSSI), such behaviors primarily serve an affect-regulation function, helping individuals manage intense negative emotions such as sadness, anger, emptiness, or psychological tension [14]. This result is also consistent with previous studies indicating that Non-Suicidal Self-Injury (NSSI) behaviors in adolescents often begin at lower or moderate frequencies and may escalate if underlying emotional distress is not adequately addressed [15].
The correlation analysis revealed a statistically significant and very strong positive relationship between hopelessness and NSSI (r = 0.876; p-value < 0.001), with median scores and Interquartile Ranges (IQR) of 11 (8) and 11 (11) respectively. This result supports the hopelessness theory, which emphasizes that negative expectations about the future and a sense of futility can lead individuals to adopt maladaptive coping behaviors, including self-injury [13]. The present findings are also in accordance with previous studies, which identified hopelessness as a significant predictor of Non-Suicidal Self-Injury (NSSI) among adolescents [8,16]. Clinically, this very strong correlation indicates that higher levels of hopelessness are closely associated with greater tendencies and severity of self-injurious behavior, highlighting the importance of assessing hopelessness as a key risk indicator in adolescent mental health and psychiatric nursing practice. Consequently, it is imperative for nursing professionals to integrate hopelessness screening into routine adolescent health assessments, focusing on fostering hope and resilience to mitigate the risk of self-injurious behaviors.
CONCLUSION
This study shows a significant and very strong positive relationship between hopelessness and Non-Suicidal Self-Injury (NSSI) among adolescents. Adolescents with higher levels of hopelessness tend to exhibit higher frequency and severity of self-injurious behavior. These findings indicate that negative expectations about the future and feelings of helplessness play a crucial role in the development of maladaptive coping strategies, particularly Non-Suicidal Self-Injury (NSSI).
The predominance of moderate levels of hopelessness and moderate frequency of Non-Suicidal Self-Injury (NSSI) suggests that emotional distress and self-injurious behavior are already present at a considerable level among adolescents. This highlights the importance of early identification and intervention within school settings.
Assessing hopelessness as a key psychological risk factor is essential in mental health screening and psychiatric nursing practice. Interventions focusing on enhancing hope, strengthening positive future orientation, and promoting adaptive coping strategies are necessary to prevent the escalation of self-injurious behavior and to improve adolescents’ psychological well-being.
Limitations
This study has several limitations. First, the use of a cross-sectional design does not allow for causal conclusions between hopelessness and Non-Suicidal Self-Injury (NSSI). Second, the data were collected using self-report questionnaires, which may be subject to response bias and social desirability, especially given the sensitive nature of self-injurious behavior. Third, although this study involved three different schools, the findings may still have limited generalizability to adolescents in diverse geographical or cultural contexts beyond the study area. Fourth, this study did not control for potential confounding variables, such as symptoms of depression or anxiety, which are known to be significantly associated with both hopelessness and NSSI behavior.
Despite these limitations, this study has notable strengths, including the use of instruments (BHS and ISAS) that have been culturally adapted and validated for the Indonesian adolescent population. Furthermore, the sample size (N = 138) is robust and highly adequate for correlational analysis, providing strong statistical power for the identified relationships. The focus on clinically relevant psychological variables contributes to a deeper understanding of adolescent mental health and provides a solid basis for future research and intervention development.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Local Ethics Committee approval
This research was approved by the Health Research Ethics Committee of the Faculty of Health, Institut Teknologi Sains dan Kesehatan (ITSKes) Insan Cendekia Medika Jombang on January 12, 2026, with the issuance of protocol number KEPK/ICME/031/I/2026.
Conflict of interest
The authors report no conflict of interest.
Authors’ contribution
Dessy Ekawati (DE) contributed to the conception and design of the study, data collection, data analysis, interpretation of the results, and manuscript drafting.
Agustina Maunaturrohmah (AM) contributed to data collection, data analysis, and critical revision of the manuscript.
Anin Wijayanti (AW) contributed to the study design, supervision, and review of the manuscript.
Ifa Nofalia (IF) contributed to data interpretation and manuscript revision.
All authors read and approved the final version of the manuscript.
Acknowledgements
The author would like to express sincere gratitude to all respondents who participated in this study, as well as to the school authorities for their support and cooperation during the data collection process. Appreciation is also extended to colleagues and mentors who provided valuable input and guidance throughout the research process.
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Health-Related Quality of Life, Sleep Disturbance, and Perceived Stress in Italian Adults Undergoing Dialysis: A Nationwide Descriptive Cross-Sectional Study
Ivan Rubbi 1†, Roberto Lupo 2†, Ritiana Marinelli 3, Federico Cucci 4*, Stefano Botti 5, Carmela
Triglia 6, Antonino Calabrò 7, Luana Conte 8,9‡, Elsa Vitale 10‡
- Department of Medical and Surgical Sciences, School of Nursing, University of Bologna, 40126 Bologna, Italy.
- Department of Surgery, ‘San Giuseppe da Copertino’ Hospital, Local Health Authority (ASL) of Lecce, 73100 Lecce, Italy.
- RSA “Oasi – Centro per la Terza Età”, Residential Socio‑Healthcare Facility, Via della Resistenza 105, 70013 Castellana Grotte, Bari, Italy.
- Città di Lecce Hospital, GVM Care & Research, 73100 Lecce, Italy.
- Hematology Unit, IRCCS Reggio Emilia Local Health Authority (Azienda USL), 42122 Reggio Emilia, Italy.
- Rizzoli Orthopedic Institute, Hospital of Argenta, Ferrara Local Health Authority, 44100 Ferrara, Italy.
- Department of Mental Health, Community Mental Health Center (CSM) of Biella, ASL BI, 13900 Biella, Italy.
- Laboratory of Advanced Data Analysis for Medicine (ADAM) at DReAM, University of Salento and Local Health Authority, "V. Fazzi" Hospital, 73100 Lecce, Italy.
- Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
- Directorate of Health and Nursing Professions, Local Health Authority of Bari, 70100 Bari, Italy.
* Corresponding author: Federico Cucci, Città di Lecce Hospital, GVM Care & Research, 73100 Lecce, Italy. E-mail: fcucci@gvmnet.it
† These authors contributed equally to this work as first author.
‡ These authors contributed equally to this work as last author.
Cite this article
ABSTRACT
Background: Patients undergoing dialysis often experience reduced health-related quality of life, poor sleep quality, and increased perceived stress. These dimensions are closely interconnected and represent key aspects of holistic patient care. From a nursing perspective, their assessment is essential to support patient-centered interventions and improve clinical outcomes.
Aim: To evaluate health-related quality of life, sleep quality, and perceived stress in patients undergoing dialysis, and to explore differences across sociodemographic and clinical subgroups.
Methods: A cross-sectional study was conducted among adult patients aged 18 years or older undergoing dialysis. Data were collected using validated instruments, including the Short Form-36 Health Survey developed within the International Quality of Life Assessment Project, the Pittsburgh Sleep Quality Index, and the Perceived Stress Scale 10-item version. Descriptive statistics were computed, and inferential statistical analyses were performed to explore differences between groups and associations between variables. Comparisons between categorical variables were conducted using chi-square tests, while differences in continuous variables between groups were assessed using independent samples t-tests and analysis of variance. Correlations between variables were evaluated using Pearson correlation coefficients.
Results: A total of 148 patients were included. Overall, participants reported reduced quality of life, poor sleep quality, and moderate to high levels of perceived stress. Significant differences emerged across sociodemographic and clinical subgroups, particularly in relation to geographical area and selected clinical characteristics. Significant associations were also observed between quality-of-life domains, sleep quality, and perceived stress.
Conclusions: Patients undergoing dialysis experience multiple interrelated challenges affecting their well-being. From a nursing perspective, the systematic assessment of quality of life, sleep quality, and perceived stress represents a key component of comprehensive care. These findings support the role of nurses in identifying patient needs, guiding personalized care planning, and implementing targeted interventions aimed at improving overall patient outcomes.
Keywords: chronic kidney disease, dialysis, health-related quality of life, sleep quality, perceived stress, nursing.
INTRODUCTION
Chronic kidney disease (CKD) is defined by a persistent reduction in renal function, typically identified by a glomerular filtration rate below 60 mL/min/1.73 m² [1]. Affecting approximately 10% of the global population [2], CKD represents a major public health concern. The progressive increase in patients requiring renal replacement therapies, including dialysis, reflects an epidemiological context characterized by population aging and a high prevalence of cardiovascular and metabolic comorbidities [3]. Beyond its clinical burden, CKD profoundly influences daily functioning, psychosocial well-being, and healthcare organization.
Dialysis, although life-sustaining, imposes a complex and enduring impact on patients’ lives. Frequent treatment sessions, dietary and fluid restrictions, physical symptoms, and technological dependence significantly reshape daily routines and limit participation in work, social, and family roles. Health-related quality of life (QoL) in this context emerges as a multidimensional construct resulting from the interaction between physical health (Physical Component Summary, PCS), psychological-emotional status (Mental Component Summary, MCS), degree of autonomy, social relationships, and treatment-related constraints.
Psychological distress is increasingly recognized in individuals undergoing dialysis. Anxiety, depressive symptoms, and perceived stress are common and often underdiagnosed, despite their association with poorer treatment adherence, reduced quality of life, and adverse clinical outcomes [4]. Similarly, sleep disturbances—including insomnia, sleep apnea, restless legs syndrome, and excessive daytime sleepiness—are highly prevalent and have been linked to increased mortality risk, impaired daily functioning, and diminished well-being [5–8]. These dimensions frequently coexist and may interact, amplifying the subjective burden of chronic kidney failure. Within the Italian healthcare system, which is predominantly public and regionally organized, dialysis services are delivered across heterogeneous clinical contexts, including hospital-based units and home-based programs. Variability in service organization, availability of psychosocial support, and access to home dialysis modalities may influence patients lived experiences. While previous Italian studies have explored specific aspects such as depressive symptoms, stress, or the impact of educational interventions [9–13], recent nationwide data simultaneously examining health-related quality of life, sleep quality, and perceived stress in a heterogeneous dialysis population remain limited. Addressing these dimensions together may provide a more comprehensive understanding of the psychosocial burden associated with dialysis treatment and support the development of multidisciplinary care strategies tailored to the Italian context.
From a nursing perspective, the assessment of health-related quality of life, sleep quality, and perceived stress represents a fundamental component of holistic care in patients undergoing dialysis. Nurses play a central role in the continuous monitoring of these dimensions, as they are directly involved in patient education, symptom management, and the identification of psychosocial needs [14]. A comprehensive understanding of these aspects is essential to support individualized care planning and to improve patient outcomes in this population.
Primary objective
The primary objective of this study was to evaluate health-related quality of life, sleep quality, and perceived stress in patients undergoing dialysis.
Secondary objectives
The secondary objectives were to explore differences in these outcomes across sociodemographic and clinical subgroups and to examine the associations between quality of life, sleep quality, and perceived stress.
MATERIALS AND METHODS
Study design
This was a descriptive cross-sectional study with exploratory analytical components, conducted between January and October 2025 using an anonymous electronic questionnaire.
Questionnaire tools
Data were collected using a structured, self-administered questionnaire consisting of five sections.
- Sociodemographic variables
The first section included direct sociodemographic variables: sex, age (recorded in years and categorized into predefined age groups), marital status, educational level (highest qualification attained), and employment status.
Clinical characteristics included the presence of comorbidities and dialysis-related variables, such as the duration of dialysis treatment. Additional clinical information relevant to patients’ health status, including conditions associated with chronic kidney disease and treatment-related aspects, was also considered. Where available, information related to symptoms commonly reported by patients undergoing dialysis and potential behavioral adaptations to chronic illness was considered.
These variables were collected to characterize the sample and to allow subsequent stratified analyses (Table 1).
- Clinical characteristics, risk factors and lifestyle
The second section comprised structured items addressing:
- main known medical condition before dialysis initiation (single-response item);
- prior awareness of dialysis (defined as patients’ prior awareness or familiarity with dialysis treatment before its initiation, derived from information received through healthcare professionals, previous clinical experiences, or informal sources);
- symptoms and signs before diagnosis (multiple-response item);
- pre-diagnosis lifestyle habits (smoking, alcohol use, illicit drug use, salt intake, weight loss);
- behavioral changes and symptoms during or after dialysis initiation (multiple-response items).
For symptom-related questions, multiple answers were allowed; therefore, percentages may exceed 100% (Tables 2 and 3).
Pre-existing medical conditions were recorded as the main perceived disease rather than as a full multimorbidity profile.
- Health-related quality of life (SF-36)
Health-related quality of life was assessed using the official Italian version of the Short Form-36 Health Survey (SF-36) (IQOLA project)[15].
The instrument consists of 36 items grouped into eight domains:
- Physical Functioning (PF) – 10 items
- Role Limitations due to Physical Health (Role Physical, RP) – 4 items
- Role Limitations due to Emotional Problems (Role Emotional, RE) – 3 items
- Vitality (VT) – 4 items
- Mental Health (MH) – 5 items
- Social Functioning (SF) – 2 items
- Bodily Pain (BP) – 2 items
- General Health (GH) – 5 items
Scores are transformed to a 0–100 scale, with higher scores indicating better perceived health status. Physical and Mental Component Summary measures (PCS and MCS) were also derived[16].
- Sleep quality (PSQI)
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. The PSQI is a validated multidimensional instrument composed of 19 self-rated items generating seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction.
The global score ranges from 0 to 21, with higher scores indicating poorer sleep quality. For analytical purposes, scores were categorized into three classes: good sleep quality (0–5), moderate impairment (6–10), and severe impairment (>10) [17].
- Perceived stress (PSS-10)
Perceived stress was measured using the 10-item version of the Perceived Stress Scale (PSS-10), developed by Cohen et al. This instrument evaluates the degree to which individuals perceive their life situations as stressful.
Items are rated on a five-point Likert scale (0 = never to 4 = very often), with four positively worded items reverse scored. Total scores range from 0 to 40, with higher scores indicating greater perceived stress. Scores were categorized as low (0–13), moderate (14–26), and high (27–40)[18].
Setting
Data were collected through an anonymous electronic questionnaire (Microsoft Forms) administered exclusively online between January and October 2025. The survey link was disseminated via major social networks (Facebook®, Instagram®, Reddit®) and through Italian provincial dialysis associations and patient support groups, allowing nationwide dissemination across Northern, Central, and Southern Italy and the Islands.
Participants completed the questionnaire independently using personal devices (smartphones, tablets, or computers), ensuring anonymity and voluntary participation.
Although data collection occurred in a virtual environment, respondents were individuals receiving dialysis treatment within established clinical settings in Italy, including hospital-based dialysis units, nephrology departments within the Italian National Health System, accredited private dialysis centers, and structured home-based dialysis programs (peritoneal dialysis and home hemodialysis). Therefore, the research setting was digital, whereas the clinical context of reference consisted of organized dialysis services within the Italian healthcare system.
The associations involved in disseminating the survey included regional branches of ANED and other Italian dialysis and transplant patient networks.
Participants
A non-probability convenience sampling strategy was adopted. Participants were recruited on a voluntary basis through online dissemination of the survey link via social networks (Facebook®, Instagram®, Reddit®) and provincial dialysis associations and patient support groups.
No formal a priori sample size calculation was performed. Given the exploratory and cross-sectional nature of the study, the aim was to obtain a nationwide snapshot of adults undergoing dialysis treatment in Italy rather than to test predefined hypotheses or estimate population parameters with predetermined statistical power. Eligible participants were adults aged 18 years or older, undergoing dialysis treatment and residing in Italy. Inclusion criteria were current dialysis treatment, residence in Italy, ability to understand the study information, and provision of electronic informed consent. Exclusion criteria were age below 18 years, failure to provide informed consent, and incomplete questionnaire completion. Participation was voluntary and unpaid. Participant recruitment, eligibility assessment, and inclusion in the final sample are summarized in a flow diagram (Figure 1).
Figure 1. Flow diagram of participant recruitment and inclusion.
Due to the open-access nature of the online survey and the anonymous recruitment process, it was not possible to determine the exact number of individuals who accessed the questionnaire or were excluded prior to completion.
The figure illustrates the online dissemination of the questionnaire, the eligibility assessment based on predefined inclusion criteria, and the final sample included in the analysis.
Statistical analysis
The dataset was created using Microsoft Excel (Microsoft Office®) and subsequently imported into Jamovi software (version 2.3.18) for statistical analysis. Descriptive statistics were performed. Continuous variables were expressed as mean ± standard deviation (SD), whereas categorical variables were reported as absolute frequencies and percentages. The internal consistency of the psychometric instruments (SF-36, PSQI, and PSS-10) was assessed using Cronbach’s alpha coefficient to evaluate their reliability within the study sample. The normality of continuous variables was assessed using the Shapiro–Wilk test and by visual inspection of histograms and Q–Q plots. Based on these assessments, parametric tests (independent samples t-test, one-way ANOVA, and Pearson’s correlation) were applied when normality assumptions were considered acceptable. Independent samples t-tests were used to compare mean SF-36 domain scores between two groups (e.g., sex, geographical area North/Central vs South/Islands, dialysis modality, number of weekly sessions). One-way analysis of variance (ANOVA) was applied to compare mean SF-36 domain scores across variables with more than two categories, specifically age groups and symptom categories reported during or after dialysis treatment. Chi-square (χ²) tests were used to examine associations between categorical variables, including PSQI and PSS-10 categories across sociodemographic and clinical groups. Correlations between continuous variables (PSQI total score, PSS-10 total score, and SF-36 domain scores) were analyzed using Pearson’s correlation coefficient (r). For analytical purposes, the geographical variable was dichotomized into two macro-areas (North/Central vs South/Islands). This grouping was adopted to ensure adequate sample size within each category and to improve the statistical stability of comparisons, given the relatively small sample size and the uneven distribution of participants across regions. All tests were two-tailed, and statistical significance was set at p-value < 0.05.
RISULTS
Sample characteristics
Although some deviations from normality were observed, parametric tests were considered appropriate given the sample size and the robustness of these methods.
Sociodemographic and clinical characteristics of our sample were reported in Table 1.
Socio-demographic characteristics n (%) Geographical area North
Central
South and Islands
51(34.5)
17(11.5)
80(54.1)
Gender Female
Male
75(50.7)
73(49.3)
Age 21-30 years
31-40 years
41-50 years
51-60 years
61-70 years
Over 71 years
11(7.4)
21(14.2)
36(24.3)
45(30.4)
23(15.5)
12(8.1)
Civil status Married
Unmarried
Divorced/Separated
84(56.8)
41(27.7)
23(15.5)
Educational level No qualifications
Middle school diploma
High school diploma
Bachelor's degree
Postgraduate education
4(2.7)
33(22.3)
64(43.2)
36(24.3)
11(7.4)
Work employment Housewife/househusband
Public employee
Freelancer
Factory worker
Retired
Student
Other occupation
16(10.8)
17(11.5)
16(10.8)
20(13.5)
41(27.7)
6(4.1)
32(21.6)
How old were you when you started dialysis? 43.9±14.7 Actually you: await a kidney transplant
already undergo a kidney transplant
undergo Automated Peritoneal Dialysis (APD)
undergo Continuous Ambulatory Peritoneal Dialysis (CAPD)
undergo home hemodialysis
undergo assisted home hemodialysis
undergo in-center hemodialysis
5(3.4)
6(4.0)
18(12.1)
10(6.8)
5(3.4)
4(2.7)
100(67.6)
Presence of symptoms/signs during or after dialysis Yes
No
81(54.7)
67(45.3)
How often do you undergo dialysis treatment? Every day
5 times/week
4 times/week
3 times/week
2 times/week
1 time/week
22(14.9)
4(2.7)
11(7.4)
92(62.2)
11(7.4)
8(5.4)
Table 1. Sociodemographic and clinical characteristics of the sample (n = 148)
A total of 148 participants were included in the analysis; of these, 50.7% were female (n=75). The most represented age group was 51–60 years, accounting for 30.4% of the sample and 45 participants, followed by 41–50 years with 24.3% and 36 participants, and 61–70 years with 15.5% and 23 participants. Most respondents were married, representing 56.8% and 84 individuals, whereas 27.7%, corresponding to 41 participants, were unmarried and 15.5%, corresponding to 23 participants, were divorced or separated.
Regarding educational level, the largest proportion of participants completed lower secondary school, accounting for 43.2% and 64 individuals, followed by upper secondary school with 24.3% and 36 individuals. A minority held a postgraduate degree, representing 7.4% and 11 participants. Retirement was the most common occupational status, accounting for 27.7% and 41 individuals, followed by factory work with 13.5% and 20 individuals, other occupations with 21.6% and 32 individuals, public employment with 11.5% and 17 individuals, and self-employment with 10.8% and 16 individuals. Only 4.1% of the sample, corresponding to 6 participants, were students.
The mean age at dialysis initiation was 43.9 years with a standard deviation of 14.7 years. At the time of survey completion, 67.6% of participants, corresponding to 100 individuals, were receiving in-centre hemodialysis.
The remaining participants were distributed across automated peritoneal dialysis, accounting for 12.1% and 18 individuals; continuous ambulatory peritoneal dialysis, 6.8% and 10 individuals; home hemodialysis, 3.4% and 5 individuals; and assisted home hemodialysis, 2.7% and 4 individuals. A small proportion reported being on the transplant waiting list, representing 3.4% and 5 individuals, while 4.0% and 6 individuals had already undergone kidney transplantation.
Most patients reported three dialysis sessions per week, accounting for 62.2% and 92 individuals. Smaller proportions underwent four sessions per week, representing 7.4% and 11 individuals; five sessions per week, 2.7% and 4 individuals; daily dialysis, 14.9% and 22 individuals; or lower frequencies of one to two sessions per week, representing 12.8% and 19 individuals.
Overall, 54.7% of the sample, corresponding to 81 participants, reported at least one symptom or sign during or after the dialysis session.
Medical history and lifestyle before dialysis initiation
More than half of the sample, representing 54.0% and corresponding to 80 participants, reported a known diagnosis of kidney failure before starting dialysis (Table 2). Hypertension was indicated as the main pre-existing medical condition by 18.9% of participants, corresponding to 28 individuals. Smaller proportions reported diabetes mellitus, accounting for 6.8% and 10 individuals, or no other medical condition, representing 11.5% and 17 individuals, indicating the absence of comorbidities beyond chronic kidney disease. Most participants, corresponding to 68.2% and 101 individuals, stated that they had heard about dialysis before initiating treatment, suggesting a certain degree of prior awareness of the therapeutic pathway. The most frequently reported symptoms before diagnosis were marked fatigue, accounting for 23.6% and 35 individuals, and swelling of the feet and ankles, reported by 22.9% and 34 individuals. Other symptoms, including nausea, vomiting, reduced urine output, itching, diffuse cramp-like pain, insomnia, or psychological complaints, were reported less frequently. A non-negligible proportion of participants reported no specific symptoms or only vague complaints.
Regarding lifestyle behaviors, 37.8% of participants, corresponding to 56 individuals, had smoked before diagnosis; 28.4%, corresponding to 42 individuals, reported alcohol consumption; and 12.2%, corresponding to 18 individuals, reported illicit drug use. Most participants did not experience marked weight loss prior to dialysis, accounting for 72.3% and 107 individuals, and 70.3%, corresponding to 104 individuals, did not habitually consume high-salt foods. Nevertheless, 85.1% of the sample, corresponding to 126 participants, stated that they had never received structured dietary counselling after diagnosis.
n (%) Main known medical condition before starting dialysis: Diabetes mellitus
Kidney failure
High blood pressure
No other medical condition
Other medical conditions
10(6.8)
80(54.0)
28(18.9)
17(11.5)
13(8.8)
Before dialysis, had you ever heard of dialysis? Yes
No
101(68.2)
47(31.8)
What were symptoms and signs before the diagnosis?
FatigueSwelling in feet and anklesAnxietyHeart palpitationsRenal colicMental confusionInvoluntary muscle contractionsRoutine checkups because my father had polycystic kidney disease
Depression
Diarrhea
Diffuse cramp-like pain
Postpartum hemorrhage
I was young when I found out. I suffered from urinary tract infections
Urinalysis
Bladder inflammation
Pregnancy
Insomnia
Red spots on lower limbs
Congenital disease
Loss of appetite
Nausea and vomiting
No symptoms
None
Vision problems
Heart problems
Protein in urine
Itching
Reduced urine output
Cyst rupture and severe bleeding, causing skyrocketing values
I have been suffering from CRF for a year, I already knew
Missing
35(23.6)
34(22.9)
1(0.7)
1(0.7)
1(0.7)
1(0.7)
4(2.7)
1(0.7)
1(0.7)
1(0.7)
3(2.0)
1(0.7)
1(0.7)
1(0.7)
1(0.7)
1(0.7)
5(3.4)
1(0.7)
1(0.7)
5(3.4)
19(12.8)
2(1.4)
3(2.0)
1(0.7)
1(0.7)
1(0.7)
1(0.7)
13(8.8)
1(0.7)
3(2.0)
3(2.0)
Had you ever smoked before your diagnosis? Yes
No
56(37.8)
92(62.2)
Had you ever consumed alcohol prior to your diagnosis? Yes
No
42(28.4)
106(71.6)
Had you used drugs before your diagnosis? Yes
No
18(12.2)
130(87.8)
Had you lost weight before undergoing dialysis? Yes
No
41(27.7)
107(72.3)
Did you consume foods high in salt before the onset of the disease? Yes
No
44(29.7)
104(70.3)
After your diagnosis, did you ever receive any dietary advice for your health problem? They told me to eat less salt, no bouillon cubes, canned
Foods, or cured meats
Nephrologist, and then I went to a nutritionist
No
Yes
1(0.7)
1(0.7)
7(4.7)
13(8.8)
126(85.1)
Table 2. Medical history and lifestyle before dialysis initiation.
Note to Table 2
Participants could report more than one symptom/sign; therefore, the total number of responses exceeds the sample size and percentages do not sum to 100%. Participants were asked to report the main known medical condition before the onset of chronic kidney disease (single-response item); therefore, these data do not capture the full burden of multimorbidity. The modality: “No other medical condition” indicates the absence of comorbidities other than chronic kidney disease.
Lifestyle, symptoms, and perceived health after dialysis initiation
Following diagnosis and dialysis initiation, clear changes in health-risk behaviors were observed (Table 3). A total of 81.8% of participants, corresponding to 121 individuals, reported no longer smoking; 87.8%, corresponding to 130 individuals, no longer consumed alcohol; and 90.5%, corresponding to 134 individuals, no longer used illicit drugs, suggesting a shift towards healthier lifestyles. With respect to body weight, 59.5% of the sample, corresponding to 88 participants, reported weight loss since starting dialysis, in many cases exceeding 6 kilograms and, for a relevant minority, more than 20 kilograms. Most participants, representing 83.8% and 124 individuals, stated that they no longer consumed high-salt foods, in line with nutritional recommendations for chronic kidney disease. During or after dialysis sessions, 54.7% of participants, corresponding to 81 individuals, experienced at least one treatment-related symptom. The most frequently reported complaints were swelling of the feet and ankles, accounting for 12.8% and 19 individuals; hypotension or blood pressure drop, 7.4% and 11 individuals; reduced urine output, 6.0% and 9 individuals; and headache, 6.8% and 10 individuals.
n (%) Did you continue smoking after your diagnosis? Yes
No
27(18.2)
121(81.8)
Did you continue to drink alcohol after your diagnosis? Yes
No
18(12.2)
130(87.8)
Did you continue to take drugs after your diagnosis? Yes
No
14(9.5)
134(90.5)
Indicate weight loss since diagnosis (for those who answered NO to the previous question, select the answer NONE)
None
0-5 kg
6-10 kg
11-20 kg
Over 21 kg
60(40.5)
18(12.2)
48(32.4)
8(5.4)
14(9.5)
Have you lost weight since starting dialysis? Yes
No
88(59.5)
60(40.5)
After your diagnosis, did you continue to eat foods high in salt? Yes
No
24(16.2)
124(83.8)
What symptoms and signs did you experience during or after dialysis treatment? Swelling in feet and ankles
Anxiety
Asthenia
Drop in blood pressure
Mental confusion
Involuntary muscle contractions
Diffuse cramp-like pain
Insomnia
Hypotension
Hypotension, fatigue
Low back pain
But in the head
Loss of appetite
Nausea and vomiting
None
Low blood pressure
Reduced urine output
Restless legs syndrome
Constipation
Fatigue and weakness
No symptoms
19(12.8)
1(0.7)
1(0.7)
5(3.4)
3(2.0)
2(1.4)
2(1.4)
1(0.7)
1(0.7)
1(0.7)
1(0.7)
10(6.8)
7(4.7)
1(0.7)
2(1.4)
11(7.4)
9(6.0)
1(0.7)
2(1.4)
1(0.7)
67(45.2)
Perceived health status Very good
Good
Bad
Very bad
17(11.5)
71(47.9)
50(33.8)
10(6.8)
Table 3. Lifestyle, symptoms, and perceived health status after dialysis initiation.
Other symptoms, including asthenia, diffuse cramps, gastrointestinal disturbances, restless legs syndrome, sleep problems, or psychological symptoms, were reported less frequently. Despite this symptom burden, 58.9% of participants, corresponding to 86 individuals, rated their health status as very good or good, whereas 41.0%, corresponding to 60 individuals, perceived their health as bad or very bad.
Psychometric properties of the instruments and score distribution
Table 4 summarizes the internal consistency of the study instruments in the present sample and the distribution of their scores, reported to document measurement reliability and to describe the clinical profile of the study population.
Scale n % Cronbach’s alpha (a) PSQI (Pittsburgh Sleep Quality Index) 0.724 Good sleep quality -- -- Moderately impaired sleep quality 76 51.4 Severely impaired sleep quality 72 48.6 PSS-10 (10-item Perceived Stress Scale) 0.728 Low 15 10.1 Moderate 106 71.6 High 27 18.2 SF-36 (Short Form-36 Health Survey) M SD Cronbach’s alpha (a) Physical Functioning (PF) 48.8 29.0 0.911 Role limitations due to physical health, (RP) 30.6 38.0 0.845 Role limitations due to emotional problems (RE) 36.5 41.4 0.822 Vitality (VT) 42.3 20.5 0.684 Mental Health (MH) 49.8 22.0 0.766 Social Functioning (SF) 52.4 23.0 0.716 Bodily Pain (BP) 62.1 25.2 0.878 General Health (GH) 33.5 20.5 0.648 Total 44.1 19.5 0.932 Table 4. Psychometric properties and score distribution of the instruments (PSQI, PSS-10, SF-36).
The Pittsburgh Sleep Quality Index demonstrated good internal consistency, with Cronbach’s alpha coefficient of 0.724. According to the predefined cut-offs, none of the participants fell within the good sleep quality category. Moderate sleep impairment was observed in 51.4% of the sample, corresponding to 76 individuals, while 48.6%, corresponding to 72 individuals, presented severe impairment. These findings indicate that clinically relevant sleep disturbances were highly prevalent within the sample.
The Perceived Stress Scale 10-item version showed satisfactory internal consistency, with a Cronbach’s alpha of 0.728. Low stress levels were reported by 10.1% of participants, corresponding to 15 individuals. The majority of the sample presented moderate stress, accounting for 71.6% and 106 individuals, whereas 18.2%, corresponding to 27 individuals, reported high perceived stress.
Regarding the SF-36, internal consistency coefficients across domains ranged from 0.648 for General Health to 0.911 for Physical Functioning, indicating overall acceptable to excellent reliability. The total SF-36 score showed excellent internal consistency, with a Cronbach’s alpha of 0.932.
The mean overall SF-36 score was 44.1 with a standard deviation of 19.5, suggesting a moderate level of health-related quality of life. Domain-level analysis revealed the lowest scores in Role Physical, with a mean of 30.6 and a standard deviation of 38.0, Role Emotional, with a mean of 36.5 and a standard deviation of 41.4, and General Health, with a mean of 33.5 and a standard deviation of 20.5. These findings indicate substantial limitations in both physical and emotional role functioning and in overall health perception. Conversely, Bodily Pain showed relatively higher scores, with a mean of 62.1 and a standard deviation of 25.2, and Social Functioning a mean of 52.4 with a standard deviation of 23.0, suggesting comparatively better preservation of these domains. Vitality and Mental Health displayed intermediate values, with means of 42.3 and 49.8, respectively, indicating moderate impairment in energy levels andmental health.
Health-related quality of life, sleep quality, and perceived stress across sociodemographic and clinical subgroups
Differences in SF-36 domain scores across sociodemographic and clinical variables are presented in Table 5.
SF – 36 (Mean±SD) PF RP RE VT MH SF BP GH Geographic area North and Central 54.4±31.3 39.0±40.4 44.6±41.0 42.6±21.4 50.8±24.0 54.6±23.4 63.5±24.5 36.2±20.8 South and Islands 44.1±26.2 23.4±34.5 29.6±40.7 42.1±19.9 48.9±20.3 50.6±22.8 61.0±25.9 31.3±20.2 p-value
t = 2.19; p = <0.03* t = 2.52; p = 0.01*
t = 2.23; p = 0.02* t = 0.13; p = 0.89 t = 0.51; p = 0.61 t = 1.04; p = 0.29 t = 0.60; p = 0.54 t = 1.44; p = 0.15 Age group 21-30 35.0±31.7 22.7±32.5 18.2±31.1 42.7±24.4 38.9±27.0 36.4±23.4 59.5±33.1 34.1±22.1 31-40 62.1±30.2 45.2±41.5 57.1±38.2 48.3±16.1 57.5±17.0 57.7±20.3 69.3±22.3 39.1±15.6 41-50 51.4±29.6 25.0±29.9 35.2±39.0 36.7±22.5 45.7±24.1 51.0±22.6 60.3±24.3 31.9±19.7 51-60 47.9±24.9 26.7±38.2 36.3±44.3 43.4±19.7 49.0±19.8 51.1±24.4 55.3±25.6 29.7±21.0 61-70 43.7±30.1 35.0±43.4 31.4±42.0 43.0±20.2 53.7±22.4 57.5±21.5 69.3±23.1 37.0±22.5 p-value
F = 2.15; p = 0.07 F = 1.34; p = 0.25
F = 2.04; p = 0.09 F = 1.19; p = 0.32 F = 1.98; p = 0.10 F = 2.18; p = 0.07 F = 2.08; p = 0.08 F = 1.26; p = 0.29 Gender Female 55.5±28.3 30.3±36.8 35.1±39.9 41.3±21.7 49.3±23.1 51.3±23.5 60.7±24.7 31.7±20.4 Male 42.0±28.3 30.8±39.4 37.9±43.1 43.4±19.2 50.2±21.1 53.6±22.7 63.6±25.9 35.5±20.7 p-value
t = 2.89; p = <0.01** t = -0.07; p = 0.93
t = -0.40; p = 0.68 t = -0.60; p = 0.52
t = -0.23; p = 0.81 t = -0.59; p = 0.55 t = -0.68; p = 0.49 t = -1.13; p = 0.26 Dialysis modality (‡) Peritoneal Dialysis 57.3±30.2 34.8±38.7 38.1±37.1 45.2±17.3 55.6±19.8 50.4±23.4 64.1±23.9 35.4±17.9 Hemodialysis 45.0±27.9 28.4±36.9 33.6±41.7 40.2±21.0 47.3±22.7 51.4±22.8 60.8±25.1 32.3±21.3 p-value
t = 2.04; p = <0.04* t = 0.80; p = 0.42 t = 0.51; p = 0.60 t = 1.14; p = 0.25 t = 1.76; p = 0.08 t = -0.19; p = 0.84 t = 0.62; p = 0.53 t = 0.69; p = 0.48 Dialysis sessions per week ≤3 sessions - week 47.6±28.4 31.1±38.3 37.5±43.4 41.5±21.0 48.1±22.6 53.7±22.3 63.4±23.9 34.3±21.0 3 sessions-week 52.6±30.8 29.1±37.5 33.3±35.1 44.9±19.1 54.6±19.6 48.6±25.1 58.4±29.0 31.4±19.3 p-value
t = -0.90; p = 0.36
t = 0.28; p = 0.78 t = 0.53; p = 0.59
t = -0.86; p = 0.38
t = -1.54; p = 0.12 t = 1.16; p = 0.24 t = 1.04; p = 0.29 t = 0.75; p = 0.45 Symptoms None 48.8±28.4 37.5±47.9 41.7±50.0 57.5±28.4 74.0±16.5 65.6±23.7 83.1±33.8 20.0±17.8 Gastrointestinal symptoms 46.8±29.3 33.8±37.4 40.0±39.9 51.8±16.7 50.2±20.3 55.6±23.1 62.1±27.9 42.8±19.8 Hypotension-hemodynamic instability 49.5±37.8 40.9±39.2 54.5±47.8 48.2±18.2 57.1±22.3 59.1±33.1 63.0±29.9 32.7±21.5 Musculoskeletal pain 52.5±37.2 28.1±41.1 37.5±45.2 54.4±17.4 58.5±18.0 56.3±25.0 59.4±20.0 35.0±21.9 Fatigue and weakness 47.4±26.8 23.4±34.7 29.4±40.0 35.5±20.5 44.1±22.0 48.5±21.2 58.6±23.5 28.8±19.7 Psychological 46.1±30.4 39.5±40.2 36.8±38.3 42.6±17.8 52.0±19.4 46.7±18.6 64.6±26.9 40.0±19.9 Other 65.0±28.7 50.0±48.6 60.0±43.9 52.5±17.4 63.2±22.1 71.3±21.3 77.3±21.1 44.0±19.3 p-value
F = 0.60; p = 0.72
F = 1.26; p = 0.27 F = 1.31; p = 0.25 F = 3.89; p <0.01**
F = 2.93; p = 0.01* F = 2.55; p = 0.04* F = 1.37; p = 0.22 F = 2.53; p = 0.02* Table 5. Differences in SF-36 domain scores across sociodemographic and clinical subgroups.
Note to Table 5
SF-36 = Short Form-36 Health Survey; PF = Physical functioning; RP = Role limitations due to physical health; BP = Bodily pain; GH = General health; VT = Vitality; SF = Social functioning; RE = Role limitations due to emotional problems; MH = Mental health;
Independent samples t-tests were used for dichotomous variables, whereas one-way ANOVA was applied to variables with more than two categories, namely age groups and symptom categories.
- Geographical area. Patients living in Northern and Central Italy displayed significantly higher mean scores than those from Southern Italy and the Islands for physical functioning, with a mean of 54.4 and a standard deviation of 31.3 compared with a mean of 44.1 and a standard deviation of 26.2, with a p-value lower than 0.03. Significant differences were also observed for role limitations due to physical health, with mean values of 39.0 and 23.4 and standard deviations of 40.4 and 34.5 respectively, with a p-value of 0.01, and for role limitations due to emotional problems, with mean values of 44.6 and 29.6 and standard deviations of 41.0 and 40.7 respectively, with a p-value of 0.02.
Sleep quality also differed significantly across geographical areas. Participants from Northern and Central regions more frequently showed moderate impairment and less frequently severe impairment compared with those from Southern and Island regions, with a p-value of 0.04, suggesting poorer sleep quality in the latter group. No statistically significant differences emerged for perceived stress levels.
- Age classes. No statistically significant differences in SF-36 domains, PSQI categories, or PSS-10 levels were observed across age groups, as all p-values were greater than 0.05. Nevertheless, some variation in mean scores was observed at a descriptive level.
- Women reported significantly better physical functioning than men, with a mean of 55.5 and a standard deviation of 28.3 compared with a mean of 42.0 and a standard deviation of 28.3, with a p-value lower than 0.01. No significant differences were observed for the remaining SF-36 domains. Sleep quality, however, was significantly worse among women, as the prevalence of severe sleep impairment was higher in females than in males, with a p-value lower than 0.01. Perceived stress levels did not differ significantly by gender.
- Dialysis modality. Patients undergoing peritoneal dialysis showed significantly higher physical functioning scores than those undergoing hemodialysis, with mean values of 57.3 and 45.0 and standard deviations of 30.2 and 27.9 respectively, with a p-value lower than 0.04. No statistically significant differences were observed for the other SF-36 domains, PSQI categories, or PSS-10 levels.
- Number of sessions per week. No significant differences were found in health-related quality of life, sleep quality, or perceived stress between patients undergoing three or fewer sessions per week and those undergoing more than three sessions per week, as all p-values were greater than 0.05.
- Presence and type of symptoms. The presence and type of symptoms during or after dialysis were significantly associated with several SF-36 domains. Significant associations were observed for vitality, with a p-value lower than 0.01; emotional well-being, with a p-value of 0.01; social functioning, with a p-value of 0.04; and general health, with a p-value of 0.02. Patients reporting fatigue and weakness, psychological symptoms, or more complex symptom clusters tended to show lower scores in these domains compared with asymptomatic patients or those reporting predominantly gastrointestinal symptoms.
The distribution of sleep quality (PSQI categories) and perceived stress (PSS-10 levels) across the same subgroups is reported in Table 6. Table 6 shows that sleep quality differed significantly according to geographical area, gender, and symptom burden. Participants living in Southern Italy and the Islands, women, and patients reporting fatigue, weakness, or psychological symptoms were more likely to experience severe sleep impairment. By contrast, perceived stress levels did not differ significantly across most sociodemographic and clinical subgroups, although patients with greater symptom burden tended to report higher stress levels. In particular, fatigue and weakness were more frequently associated with high perceived stress.
PSQI (Sleep quality) PSS-10 (Stress level) Moderately Severely Low Moderate High n (%) n (%) Geographical area North and Central Italy 41(53.9) 27(37.5) 11(73.3) 44(41.5) 13(48.1) South and Islands 35(46.1) 45(62.5) 4(26.7) 62(58.5) 14(51.9) c² = 4.03; p-value = 0.04* c² = 5.42; p-value = 0.66 Age group 21-30 3(3.9) 8(11.1) 1(6.7) 7(6.6) 3(11.1) 31-40 12(15.8) 9(12.5) 4(26.7) 12(11.3) 5(18.8) 41-50 21(27.6) 15(20.8) 3(20.0) 26(24.5) 7(25.9) 51-60 25(32.9) 20(27.8) 2(13.3) 34(32.1) 9(33.3) 61-70 15(19.7) 20(27.8) 5(33.3) 27(25.5) 3(11.1) c² = 4.87; p-value = 0.31 c² = 7.51; p-value = 0.48 Gender Female 28(36.8) 47(65.3) 8(53.3) 51(48.1) 16(59.3) Male 48(63.2) 25(34.7) 7(46.7) 55(51.9) 11(40.7) c² = 12.0; p-value = <0.01** c² = 1.12; p-value = 0.57 Dialysis modality (‡) Peritoneal Dialysis 14(19.7) 14(21.2) 5(35.7) 19(19.4) 4(16.0) Hemodialysis 57(80.3) 52(78.8) 9(64.3) 79(80.6) 21(84.0) c² = 0.04; p-value = 0.82 c² = 2.38; p-value = 0.30 Dialysis sessions per week ≤ 3 sessions - week 56(73.7) 55(76.4) 8(53.3) 83(78.3) 20(74.1) 3 sessions - week 20(26.3) 17(23.6) 7(46.7) 23(21.7) 7(25.9) c² = 0.14; p-value = 0.70 c² = 4.38; p-value = 0.11 Symptoms None 4(5.3) -- 2(13.3) 1(0.9) 1(3.7) Gastrointestinal symptoms 12(15.8) 8(11.1) 2(13.3) 15(14.2) 3(11.1) Hypotension - hemodynamicinstability 8(10.5) 3(4.2) 2(13.3) 8(7.5) 1(3.7) Musculoskeletalpain 1(1.3) 7(9.7) -- 8(7.5) -- Fatigue and weakness 37(48.7) 39(54.2) 7(46.7) 50(47.2) 19(70.4) Psychological 7(9.2) 12(16.7) -- 16(15.1) 3(11.1) Other 7(9.2) 3(4.2) 2(13.3) 8(7.5) -- c² = 14.4; p-value = 0.02* c² = 19.7; p-value = 0.07 Table 6. Distribution of sleep quality (PSQI) and perceived stress (PSS-10) across sociodemographic and clinical subgroups.
PSQI categories also differed significantly across symptom groups, with a p-value of 0.02, indicating that more symptomatic patients were more likely to experience severe sleep impairment.
The distribution of PSS-10 levels showed a near-significant trend, with a p-value of 0.07, suggesting a possible association between symptom burden and perceived stress.
Correlations between sleep quality, perceived stress, and quality of life
Table 7 presents the correlations between sleep quality measured through the Pittsburgh Sleep Quality Index, perceived stress assessed by the Perceived Stress Scale 10-item version, and selected SF-36 domains. A positive correlation emerged between PSQI and PSS-10 scores, with r equal to 0.199 and a p-value lower than 0.05. This finding indicates that poorer sleep quality, reflected by higher PSQI scores, was associated with higher levels of perceived stress. PSQI scores showed significant negative correlations with several SF-36 domains. A negative association was observed with vitality, with r equal to −0.178 and a p-value lower than 0.05, with social functioning, with r equal to −0.273 and a p-value lower than 0.05, and with bodily pain, with r equal to −0.256 and a p-value lower than 0.05. Overall, worse sleep quality was associated with lower energy levels, reduced social functioning, and a greater impact of pain. Similarly, PSS-10 scores were negatively correlated with several domains of health-related quality of life. Significant associations were found with physical functioning, with r equal to −0.376 and a p-value lower than 0.01, with role limitations due to physical health, with r equal to −0.294 and a p-value lower than 0.01, with role limitations due to emotional problems, with r equal to −0.433 and a p-value lower than 0.01, and with emotional well-being, where one of the strongest correlations was detected, with r equal to −0.676 and a p-value lower than 0.01. Additional negative correlations were found with vitality, with r equal to −0.673 and a p-value lower than 0.01, with social functioning, with r equal to −0.480 and a p-value lower than 0.01, with bodily pain, with r equal to −0.377 and a p-value lower than 0.01, and with general health, with r equal to −0.545 and a p-value lower than 0.01. These findings indicate that higher perceived stress was associated with poorer overall health perception, greater pain-related interference, reduced social participation, and broader impairments across both physical and psychological domains. Taken together, these results highlight a strong interplay between sleep quality, perceived stress, and health-related quality of life.
PSQI PSS-10 r p-value r p-value PSQI -- -- 0.199* < 0.05 SF-36 = Short Form-36 Health Survey, SF-36 Physical Functioning (PF) -0.030 0.71 -0.376* < 0.01 SF-36 Role limitations due to physical health (RP) -0.090 0.27 -0.294* < 0.01 SF-36 Role limitations due to emotional problems (RE) -0.151 0.06 -0.433* < 0.01 SF-36 Vitality (VT) -0.178* < 0.05 -0.673* < 0.01 SF-36 Mental health (MH) -0.100 0.22 -0.676* < 0.01 SF-36 Social functioning (SF) -0.273* < 0.05 -0.480* < 0.01 SF-36 Bodily Pain (BP) -0.256* < 0.05 -0.377* < 0.01 SF-36 General Health (GH) -0.061 0.46 -0.545* < 0.01 * = significant test, r = Pearson correlation coefficient Table 7. Pearson correlation analysis between sleep quality (PSQI), perceived stress (PSS-10), and SF-36 domains.
DISCUSSION
Dialysis is a life-sustaining therapy, but it entails a substantial and long-lasting burden on the everyday lives of people with chronic kidney disease. The findings of this study confirm that the dialysis experience simultaneously involves physical, psychological and social dimensions, and that the clinical management of end-stage renal disease cannot be reduced to the control of laboratory parameters alone [9,19,20].
This multidimensional burden has been widely documented in previous studies, which describe dialysis as a condition affecting physical, psychological, and social domains simultaneously, with significant implications for patients’ daily functioning and well-being [21,22].
The absence of data on dialysis vintage and caregiving support may have limited the interpretation of some findings, as these factors are known to influence patients’ adaptation to treatment and perceived burden.
Given the cross-sectional design of the study, the findings should be interpreted as associations observed at a single time point rather than causal relationships.
In our sample, SF-36 scores depict an overall moderate level of health-related quality of life, with marked impairment of role limitations due to physical and emotional problems and of general health perception. This pattern is consistent with previous studies in dialysis populations, which have shown that difficulties concern not only somatic symptom burden, but also the ability to maintain work, family and social roles that are coherent with one’s pre-morbid identity [10–13]. From this perspective, health-related quality of life emerges as the dynamic outcome of a continuous renegotiation between disease, treatment and life projects.
These findings are consistent with previous research showing that patients undergoing dialysis report significantly lower SF-36 scores compared to the general population, particularly in domains related to physical and emotional roles [23,24].
One of the most critical findings of this study is the virtual absence of “good sleepers” according to PSQI criteria and the very high prevalence of moderate or severe sleep disturbance. This observation aligns with a robust body of literature showing that sleep problems are highly prevalent among hemodialysis patients and are associated with substantially poorer quality of life across multiple domains [21,25,26].
Indeed, sleep disturbances have been reported in up to 50–80% of patients undergoing hemodialysis and are consistently associated with poorer quality of life and increased symptom burden [25,27,28].
In our sample, higher PSQI scores (worse sleep) were associated with lower scores in selected SF-36 domains, particularly vitality, social functioning and bodily pain. The association with vitality is particularly relevant, as it suggests that poor sleep may be closely linked to reduced energy levels and fatigue, which can substantially affect daily functioning and coping capacity [28].
This pattern echoes previous studies reporting significantly worse SF-36 profiles in “poor sleepers” than in “good sleepers”, and a negative correlation between global PSQI scores and overall health-related quality of life [21,25].
Clinically, sleep should therefore not be considered a secondary epiphenomenon of chronic kidney disease, but rather a relevant modulator of the dialysis experience: non-restorative sleep may reduce the cognitive and emotional resources required to cope with treatment demands, amplify fatigue, increase pain interference, and compromise the patient’s ability to sustain social participation [27,28].
This interpretation is supported by previous studies suggesting that poor sleep quality is an independent predictor of reduced quality of life and adverse clinical outcomes in dialysis populations [27]. Perceived stress, as measured by the PSS-10, showed significant associations with multiple domains of the SF-36, including physical functioning, role limitations due to physical and emotional problems, vitality, mental health, social functioning, bodily pain and general health. This is consistent with studies in hemodialysis populations reporting that higher stress levels are related to poorer quality of life and, in some cases, reduced resilience [29].
Previous evidence indicates that chronic stress in dialysis patients is associated with treatment burden, uncertainty, and reduced coping capacity, contributing to poorer psychosocial outcomes [30].
In our sample, this pattern suggests that stress may affect not only emotional adjustment but also the patient’s ability to maintain physical roles and daily functioning [29].
The most salient aspect, however, is the positive correlation between PSS-10 and PSQI scores, suggesting a bidirectional relationship between stress and sleep quality.
This bidirectional association has been previously described in the literature, where sleep disturbances and psychological distress mutually reinforce each other, creating a cycle that negatively affects both mental health and daily functioning [31]. On the one hand, chronic stress impairs the initiation and maintenance of sleep and reduces sleep depth; on the other, fragmented and non-restorative sleep weakens coping capacity, makes symptom management more difficult, and ultimately increases perceived stress. The concurrent associations observed between disturbed sleep, elevated stress and lower SF-36 scores—particularly in domains related to vitality, social functioning, pain and role functioning—suggest a potentially interrelated pattern among these dimensions, particularly involving energy levels, social participation, pain perception and role functioning within this cross-sectional sample. This model is in line with work showing that, among dialysis patients, symptom burden, poor sleep and impaired quality of life tend to co-occur and to mutually amplify one another [28]. Subgroup analyses revealed significant differences across macro-geographical areas, with poorer quality of life and worse sleep in some regions of the country. However, the interpretation of these findings should consider that geographical areas were grouped into two macro-categories for analytical purposes, which may have reduced the granularity of regional differences. These geographical differences may reflect regional variability in healthcare organization, access to home dialysis modalities, socioeconomic disparities, and availability of psychosocial support services within the Italian National Health System. However, as contextual variables were not directly measured, these interpretations remain speculative and should be explored in future analytical studies specifically designed to assess organizational and structural determinants. Although the cross-sectional design does not allow causal inferences and organizational variables were not directly measured, the observed geographical differences may reflect contextual variations that warrant further investigation in future analytical studies. This is a relatively unexplored area in Italian research and warrants further investigation from a health-equity perspective.
Differences by dialysis modality represent another clinically relevant aspect. These differences should be interpreted cautiously, as the non-probability sampling design does not allow adjustment for potential confounding variables. In our sample, patients on peritoneal dialysis reported better physical functioning than those on hemodialysis. This is coherent with studies showing more favorable quality-of-life profiles in peritoneal dialysis patients in some settings, possibly related to greater autonomy in treatment management and a stronger sense of control over daily routines [32].Symptom burden further reinforces this systemic view. Patients reporting fatigue, weakness, psychological symptoms or intradialytic/post-dialytic discomfort had markedly lower scores in vitality, mental healthand social functioning. This is consistent with the correlational findings, in which vitality emerged as a shared domain associated with both poorer sleep quality and higher perceived stress [28,29]. These findings, in line with previous work documenting the strong association between symptom distress, sleep disturbance and quality of life, support the view that symptom management is not only a biomedical objective but also a key psychosocial leverage point [28]. Taken together, our findings suggest that quality of life in dialysis patients should be conceptualised as the outcome of a dynamic system in which physical symptoms, sleep disturbance, perceived stress and contextual factors interact with each other. Within the limits of a cross-sectional design, these findings support the potential value of multidisciplinary approaches aimed at addressing sleep, stress, and symptom burden in dialysis populations. These findings are particularly relevant for nursing practice because nurses are ideally positioned to detect early changes in symptom burden, sleep quality, perceived stress, and quality of life during routine dialysis care. This perspective reinforces the role of nursing assessment as a key step in identifying unmet needs and tailoring supportive interventions. For nursing practice, at least three priority areas emerge:
- Routine screening:
1.1) systematic assessment of sleep quality (PSQI), perceived stress (PSS-10) and health-related quality of life (SF-36) as part of regular follow-up;
1.2) early identification of high-risk profiles combining poor sleep, high stress and markedly impaired quality of life.
2) Targeted interventions on sleep and stress:
2.1) tailored sleep-hygiene education that explicitly considers dialysis-related constraints (session schedules, intradialytic symptoms, daytime napping);
2.2) structured psychological and stress-management interventions (nurse-led counselling, peer groups, mindfulness-based programmes), which have shown promising effects on stress, sleep and quality of life in this population [3,33].
3) Personalisation of care pathways:
3.1) considering geographic area, dialysis modality and symptom profile when stratifying risk and designing educational and supportive pathways;
3.2) when clinically appropriate, promoting treatment options that enhance autonomy and perceived control.
In summary, the interplay between sleep, stress and quality of life observed in this study invites us to move beyond a fragmented view of care. Relatively focused interventions on sleep and coping may generate cascading benefits across the psycho-physical equilibrium of dialysis patients, potentially improving not only patient-reported outcomes but also long-term adherence and clinical trajectories.
From a nursing perspective, these findings are coherent with the rationale outlined in the Introduction, where health-related quality of life, sleep quality, and perceived stress were identified as key dimensions of holistic care in patients undergoing dialysis. The strong interrelationship observed among these variables highlights the importance of systematic assessment in routine nursing practice. Nurses play a central role in identifying sleep disturbances and psychological distress, providing patient education, and implementing supportive interventions aimed at improving coping strategies and overall well-being [14,34].
Study limitations
The decision to conduct a cross-sectional study prevents analysis and evaluation of the course of the previously listed disorders. A further limitation relates to the sample size, which is not representative of the entire Italian population undergoing dialysis treatment. Moreover, pre-existing medical conditions were assessed through a single self-reported item asking participants to indicate their main known disease before the onset of chronic kidney disease. As a result, our data do not allow a detailed quantification of multimorbidity, which is known to be highly prevalent in dialysis populations, and comorbid burden may therefore be underestimated in this sample. Furthermore, the use of convenience sampling and online recruitment may have introduced selection bias, potentially favoring individuals with greater digital literacy or engagement in patient associations. In addition, given the exploratory nature of the study and the absence of a priori hypotheses, inferential analyses were performed without adjustment for multiple comparisons. Therefore, subgroup differences and associations should be interpreted with caution and considered hypothesis-generating rather than confirmatory. Another limitation of this study is the lack of information on dialysis vintage, defined as the time elapsed since the initiation of dialysis treatment, which may significantly influence patients’ physical, psychological, and adaptive responses to therapy. In addition, variables related to the availability of informal or formal caregiving support, as well as work-related aspects such as absenteeism or presenteeism, were not assessed. These factors may play an important role in shaping patients perceived burden, quality of life, and stress levels. Future studies should incorporate these variables to provide a more comprehensive understanding of the multidimensional impact of dialysis on patients’ daily lives.
CONCLUSIONS
To our knowledge, this cross-sectional observational study represents one of the first Italian nationwide attempts to jointly assess health-related quality of life, sleep quality, and perceived stress in a heterogeneous adult dialysis population. Findings indicate a moderate overall level of health-related quality of life, with marked impairment in role limitations due to physical and emotional problems and in general health perception. Nearly all participants reported clinically relevant sleep disturbance, and more than two thirds experienced at least moderate levels of perceived stress. Significant associations between poorer sleep quality, higher perceived stress, and lower SF-36 domain scores suggest an interrelated pattern in which biological, psychological, and contextual dimensions converge to shape the lived experience of dialysis, influencing both functional capacity and psychosocial well-being. Within this framework, quality of life appears as the emergent outcome of a dynamic and multidimensional system rather than a purely physical construct. Observed differences across geographical areas and dialysis modalities, although not allowing causal inference, highlight potential contextual and organizational influences within the Italian healthcare setting and warrant further investigation.Overall, these findings support the systematic integration of sleep and stress assessment into nephrology care pathways and reinforce the value of multidisciplinary models addressing symptom burden, psychosocial distress, and patient-reported outcomes. Future longitudinal studies with larger samples are needed to clarify directional relationships and to evaluate the effectiveness of targeted interventions on patient-centered outcomes and long-term care trajectories. From a nursing perspective, the systematic assessment of quality of life, sleep quality, and perceived stress should be considered an integral part of routine dialysis care. These dimensions provide essential information to guide personalized nursing interventions, strengthen patient-centered care, and support improved clinical outcomes.
List of abbreviations
CKD – Chronic Kidney Disease;
QoL – Quality of Life
SF-36 – Short Form-36 Health Survey
IQOLA – International Quality of Life Assessment Project
PSQI – Pittsburgh Sleep Quality Index
PSS-10 – Perceived Stress Scale – 10-item version
PF – Physical Functioning
RP – Role Physical
RE – Role Emotional
VT – Vitality
MH – Mental Health
SF – Social Functioning
BP – Bodily Pain
GH – General Health
PCS – Physical Component Summary
MCS – Mental Component Summary
SD – Standard Deviation
ANOVA – Analysis of Variance
χ² – Chi-square test
r – Pearson correlation coefficient
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Ethics Approval
This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of IRCCS Istituto Oncologico “Gabriella Serio”, Bari, Italy (Protocol No. 568, data approval: July 30, 2024).
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Data Availability
The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.
Author Contributions
Ivan Rubbi and Roberto Lupo contributed to the study conceptualization and methodology, data collection, analysis and interpretation of results, and drafting of the first version of the manuscript. Luana Conte and Elsa Vitale made substantial contributions to conceptualization, methodology, scientific supervision, and critical revision of the manuscript. Ritiana Marinelli, Stefano Botti, Carmela Triglia, and Antonino Calabrò contributed to data collection and manuscript revision. Federico Cucci contributed to manuscript review and editing. All authors read and approved the final version of the manuscript. Ivan Rubbi and Roberto Lupo contributed equally as first authors. Luana Conte and Elsa Vitale contributed equally as senior authors.
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The Role of Acid–Base Understanding in Shaping Clinical Monitoring Skills Among Nurses: A Descriptive Cross-Sectional Correlational Study in Northeastern Pakistan
Muhammad Sohrab khan 1, Jihad Hussain 2, Muhammad Ishaq 2, Shams Ul Haq 2,
Hamza Khan 2, Muhammad Shayan 2, Zohaib Hussain 3, Mah Noor Mumtaz 4,
Wajid Hussain 4, Abdur Rahman 2*, Mahnoor Ali 5
- Medical B Ward, Bacha Khan Medical College / MTI Mardan Medical Complex, Mardan, Peshawar, Pakistan.
- Department of Nursing, Elizabeth Rani College of Nursing Mardan, Peshawar, Pakistan.
- Department of Nursing, Institute of Health Sciences, Mardan, Peshawar, Pakistan.
- Department of Biochemistry, Abdul Wali Khan University, Mardan, Peshawar, Pakistan.
- Department IPMH & BS, Khyber Medical University, Khyber Pakhtunkhwa, Pakistan.
* Corresponding author: Abdur Rahman, Department of Nursing, Elizabeth Rani College of Nursing Mardan, Peshawar, Pakistan. E-mail: abdurrahman888889998@gmail.com
Cite this article
ABSTRACT
Introduction: Acid-base balance is one of the most essential physiological terms, which play a crucial role in the examination of the patient and clinical observation. To control the patients well and detect changes when they start to deteriorate, nurses should have a good grasp of acid-base physiology.
Objective: The paper examined the impact of the level of knowledge of acid-base balance on clinical surveillance practices of nurses in Northeastern Pakistan.
Material & Methods: A cross-sectional, correlational, descriptive study was conducted on 403 registered nurses in different clinical settings in Northeastern Pakistan. The questionnaire was structured and validated, and it was completed by over four weeks by five sections: demographics; acid-base knowledge (10 items); clinical monitoring skills (12 items); knowledge to practice application (5 items); and barriers to effective monitoring. The statistical tests included descriptive statistics, Pearson correlation, independent t tests, one way ANOVA and linear regression.
Results: Nurses had high scores on acid-base knowledge (mean of 9.78/10, SD of 0.58) and clinical monitoring skills (mean of 53.8/60, SD of 6.2). There was a great deal of correlation between knowledge and skills (r = 0.82, p = 0.001). Greater qualification, experience, and formal training in ABG was an indicator of superior skills. Heavy workload was the most prevalent (54.1%) and lack of time (36.0%). Knowledge explained 52% of the variance in monitoring skills (R² = 0.52).
Conclusion: The clear understanding of acid-base balance is a key to the formation of clinical monitoring skills of nurses. The enhancement of patient monitoring practices will be achieved by the strengthening of acid-base education and barriers of workload.
Keywords: Clinical monitoring skills, acid -base balance, nurses, Pakistan, nursing education.
INTRODUCTION
One of the fundamental physiologic principles required during the evaluation and observation of patients is acid-base balance. As the first-line caregivers, nurses are very important in the early detection of acid-base disorders. One of their direct impacts on patient outcomes is their ability to detect subtle differences in respiratory patterns, state of consciousness, and vital signs [1]. Systematic observation, assessment, and interpretation of patient data is called clinical monitoring and is one of the core competences that distinguish expert nurses and novices and ensure patient safety [2,3]. Although the nursing curricula addresses the physiology of acid-base, there is most of the time a gap that exists between theory and practice. This gap between theory and practice is an important topic in nursing education with the potential threat to patient safety [4-6]. It has been reported that nursing students performed poorly on written exams, but they failed to apply their learning in the bedside [4,5]. Acid-base disturbances are common in the critically ill patients and are associated with a high mortality. Deviations in the parameters of arterial blood gases are also the foretellers of bad results among patients who are under intensive care [7], low blood pH is also a strong indicator of adverse final results [8]. Septic patients in Pakistan would have metabolic acidosis, and it was associated with fatal outcomes [9]. Physical examination (respiratory rate, depth, pattern, level of consciousness, vital signs) and analysis of diagnostic data, especially arterial blood gas (ABG) are part of clinical monitoring regarding acid-base balance [2,3,10]. The proper understanding of ABG requires a good theoretical foundation [10-12]. Nurses who understand the pathophysiology of acid-base have a greater advantage to diagnose the beginning of deterioration, care formulation, effective communication with the healthcare team, and predict complications [1,2,4,7-9,10-14]. There are a number of factors that determine the capability of nurses to use acid-base knowledge in clinical practice. Increased level of education, deep clinical experience, and formal training in ABG have been associated with better monitoring skills [1,3]. On the other hand, workload, time, lack of confidence, and inadequate continuing education are the obstacles [3,9,15]. Nursing education in Pakistan has evolved in the last thirty years. Pakistan Nursing Council regulates nursing education and licensure and the Generic Bachelor of Science in Nursing program includes pathophysiology and clinical rotations [3,9]. However, the evidence on the association between theoretical knowledge of acid-base and clinical monitoring skills in the Pakistani context among nurses is lacking [3,9]. Therefore, this study was aimed at exploring the role that the classification of acid-base can play in the building of clinical monitoring proficiency in nurses in Northeastern Pakistan.
Aim
This research will focus on the importance of acid-base knowledge in developing clinical monitoring competencies in nurses in Northeastern Pakistan.
Objective: The research questions of this research are as follows:
- To determine the degree of acid-base knowledge in nurses in Northeastern Pakistan.
- To determine the self-reported degree of clinical monitoring skills in nurses working in Northeastern Pakistan.
- To establish the relationship between the knowledge of acid-base and clinical monitoring skills of the nurses.
- To compare clinical monitoring skills among various demographic and professional groups such as gender, qualification, years of experience and the trainee status of ABG training.
- To determine the obstacles that do not allow the nurses to monitor patients effectively to detect acid-base imbalances.
- To produce evidence to guide nursing education and practice on the management of acid-base balances.
MATERIALS AND METHODS
Study Design and Setting
The study was a descriptive, cross-sectional, correlational study, which was carried out across four weeks among the Registered Nurses (RNs) in various clinical environments in Northeastern Pakistan. The purpose was to test the effect of the knowledge of acid-base concepts on the clinical monitoring ability of nurses.
Sampling and Eligibility
Study Period
The research was conducted for four weeks, between 1 December 2025 and 29 December 2025.
Type of Study
The relationship between acid-base knowledge and clinical monitoring competence in nurses was examined in a descriptive cross-sectional correlational design.
Study Population
The sample included RNs who were employed in a tertiary hospital, district hospital, private clinic, and community health center in Northeastern Pakistan. The student nurses, post RN nurses and diploma-nurse graduates were not included to give similar clinical experience and training.
Inclusion Criteria
- Registered Nurse and valid license.
- In use in a clinical environment.
- Willing to participate
- Capable of comprehending and filling the questionnaire.
Exclusion Criteria
- Unregistered (yet) student nurses.
- Nurses out on long leave or out during data collection.
- Informed consent was not given by nurses.
Sample Size Calculation
The OpenEpi v3.0 was used to calculate the sample size based on Cochran formula:
Where: Z = 1.96 (95% confidence level). p = 0.50 (proportion expected; this maximizes the sample size since there was no previous research in this population) and d = 0.05 (margin of error).
Since we did not know the RN total population, we did not make any correction in terms of a finite population. In this way, the minimum number of nurses was 385. All available and qualifying RNs were invited to minimize the non-response bias. There was adequate statistical power and 403 nurses were responding.
Sampling Technique
Non-probability convenience sampling technique was selected due to the practicality: access to the participants, time and the research exploratory nature. There was no possibility to use random or probability-based sampling since a full sampling frame of all registered nurses in all clinical settings in Northeastern Pakistan was not available. Moreover, the nurses were not always available on their units since they were on shifts, leaves, and caring of patients, which made the use of probability-based sampling impossible. We recognize that convenience sampling can be a source of selection bias because nurses who were available and willing to take part might differ with those who were not. This restricts the generalization of the findings.
Participants were recruited through personal contacts
Several clinical sites that were chosen offered administrative assistance to the nurses. Recruitment was done using nursing supervisors, head nurses and clinical coordinators who used the official communication channels to pass the information about the study.
Context of Participation
The paper questionnaires were issued within the clinical setting. They could be done by nurses during breaks or after shifts. The study details were provided in a concise manner and nurses were not pressured to join in the study.
Voluntary Participation
The participation was on a voluntary basis. Nurses were given a clear information regarding the objectives of the study, procedures and possible benefits and their informed consent form was signed. They were also promised that their participation would not have any impact on their jobs or professional status.
Bias Mitigation
Although convenience sampling may introduce self-selection bias, several measures were implemented to minimize this risk. All qualified nurses were invited irrespective of previous interest and knowledge. There were several reminders which increased representativeness. The fact that the study was represented by various clinical settings in the Northeastern part of Pakistan also minimized bias.
Ethical Considerations
The research received the consent of the Institutional Review Board (IRB) of Abdul Wali Khan University, Mardan.
- IRB Title: the institutional review board, Abdul Wali Khan University Mardan.
- IRB Number: IRB/2025/Acid-Base/Biochem/Nursing/0011
- Approval Date: 21-Nov-2025
The subjects were assured confidentiality, anonymity and the freedom to withdraw whenever they wished without consequences. No personal identifiable data was gathered, all the data were coded by number and safely kept by the research team.
Informed Consent
All the participants signed written consent before data collection. The consent form described the purpose of the study, which was voluntary, the right to leave, and to confidentiality.
Incentives
No economic or non-economic rewards were provided to take part.
Instruments
Data Collection Tool
Data were collected using an organized and tested questionnaire that was based on previous and confirmed research about the knowledge of acids and bases and clinical monitoring proficiencies. The questionnaire was created based on regular nursing teaching material and subject-matter experts revised the content of the questionnaires to ensure the content validity.
The questionnaire has been categorized into five parts as reported in Table 1.
Section Content Number of Items A Demographic Characteristics 6 items B Acid-Base Understanding (Knowledge Test) 10 items C Clinical Monitoring Skills (Self-Assessed Competency) 12 items D Knowledge-to-Practice Application 5 items E Barriers to Effective Monitoring 1 item (multiple response) Table 1. Section Number of Items Content.
Section A
Demographic Characteristics such as age, gender, qualification, years of experience, work area and ABG training status were included.
Section B
Acid-Base Understanding - involved ten multiple-choice question-based tests that evaluate pH, PaCO 2, -HCO 3, nature of disorders, compensatory responses, and typical clinical situations. One point was given a correct answer, making the possible score between 0 and 10.
Section C
Clinical Monitoring Skills- included twelve questions assessing self-reported abilities in respiratory assessment, ABG and risk identification, prioritisation and communication. The scale was based on a 5-point Likert scale:
- 1 Never/Not Confident,
- 2 Rarely/Slightly Confident,
- 3 Sometimes/Moderately Confident,
- 4 Often/Very Confident,
- 5 Always/Extremely Confident.
Total scores ranged from 12 to 60.
Section D
Knowledge-to-Practice Application - consisted of five questions which tested how knowledge is implemented in clinical practice.
Section E
Barriers to Effective Monitoring - had one item which provided the respondent with multi-choice possibility of selecting more than one perceived barrier.
Validity and Reliability
Two experts a nursing educator and a clinical instructor validated content validity. Face validity was developed through pilot testing ten registered nurses. Cronbach alpha (0.85) was used to measure Section C internal consistency, which was good.
Content validity was established by two experts (a nursing educator and a clinical instructor). Pilot testing was carried out to develop face validity using ten registered nurses.
The alpha of Cronbach was determined to determine the internal consistency of every section of the questionnaire:
- Section B (10 items): α = 0.81.
- Section C (12 items): α = 0.85.
- Section D (5 items): α = 0.79.
The values are all above the acceptable level of 0.70, which means good internal consistency.
Data Collection Procedure
The questionnaire was written and sent out to the clinical situations of participants. The researcher visited the selected health-care facilities at convenient times, approached the potential participants, informed them about the study, and invited them to participate in the study. Informed consent was received by means of written informed consent. The questionnaires were given out and collected on the same day to optimise the response rates. The collection of data went on till the required sample size was achieved.
Statistical Analysis
The data were read with the help of SPSS version 26. All the variables were computed using descriptive statistics. Means, standard deviations (SD), median, interquartile range (IQR), minimum, and maximum were presented as the variables were continuous (age, knowledge scores, skills scores, and so on). Categorical variables (gender, qualification, years of experience, work area, ABG training status) were described in the form of frequencies and percentages.
Shapiro-Wilk test was used to test the normality of knowledge and skills scores; p-values were found to be greater than 0.05, which proves that knowledge and skills scores were distributed normally and, accordingly, matches the use of parametric tests.
Pearson correlation coefficients were used to test a correlation between skills scores and total knowledge scores, and the assumption of linearity and normality was checked and fulfilled.
Independent-samples t-tests were applied to test the difference in mean scores of knowledge and skills between male and female nurses.
ANOVA was used to determine the difference between the mean scores of knowledges and skills by comparing groups of years of experience, the level of qualification and the area of work. The reason why the test was selected was that the independent variables had more than two levels. Premeditative homogeneity of variance and normality were investigated: the Shapiro–Wilk test demonstrated non-significant p-values (p > 0.05) in all groups, and Levene test showed that variance was equal (p > 0.05). Type I error was controlled when ANOVA showed significant differences by Tukey, Honestly Significant Difference (HSD) post-hoc tests.
The skills scores were predicted using knowledge score as the only predictor through simple linear regression. Multiple linear regression was used to determine the independent predictors of the skills scores, such as knowledge score, years of experience, qualification, and status of ABG training. All predictors were included using the enter method. Linearity, independence of residuals, homoscedasticity, and normality of residual assumptions were met. The 95% confidence interval for correlation and regression coefficients was reported to show precision.
Regression assumptions verification: The individual predictive effect of academic knowledge score of acid–base balance on clinical monitoring skills was assessed using simple linear regression. Multiple linear regression was used to determine the independent predictors of clinical monitoring skills after controlling for possible confounders, including qualification, years of experience, and ABG training status.
The linear regression assumptions were checked before analysis. Linearity was assessed by visual inspection of scatterplots of residuals against predicted values, which did not reveal any discernible pattern. Normal distribution of residuals was verified using the Shapiro–Wilk test (p > 0.05) and Q–Q plots. Homoscedasticity was assessed using the Breusch–Pagan test (p > 0.05), confirming constant variance of residuals. Multicollinearity, where multiple regression was used, was evaluated using variance inflation factor (VIF) values, which ranged from 1.12 to 1.89, indicating no significant multicollinearity. Independence of residuals was tested using the Durbin–Watson test (value = 1.98), indicating no autocorrelation.
The statistical significance level was established at p<0.05 and all the tests were two-tailed.
RESULTS
Demographic characteristics
There were 403 registered nurses who took part in the study. The average age of the sample was 34.8 (SD 7.2) and was between 22 and 52 years. The demographic characteristics are provided in Table 1, and the rest of the relevant findings are shown in Figures 1,2,3,4.
Variable Category Frequency (n) Percentage (%) Gender Male 207 51.4 Female 196 48.6 Qualification Diploma 253 62.8 BSN 117 29 MSN 33 8.2 Experience < 1 year 33 8.2 1-5 years 132 32.8 6-10 years 123 30.5 > 10 years 115 28.5 ABG Training Yes 310 76.9 No 93 23.1 Training Recency < 6 months 48 15.5 6-12 months 54 17.4 > 1 year 208 67.1 Table 1. Demographic Traits of Participants
Figure 1 indicates the gender distribution of the 403 registered nurses. The percentage distribution of males and females is 51.4 (207 nurses) and 48.6 (196 nurses), respectively, which is rather equal.
Figure 1. Gender of Participants.
Figure 2 shows the level of education of the nurses. The majority of them had a Diploma in Nursing (62.8% of 253 nurses), then a Bachelor of Science in Nursing (BSN) with 29.0% (117 nurses), and finally a Master of Science in Nursing (MSN) with 8.2% (33 nurses).
Figure 2. Qualification of the participants
Figure 3 provides the clinical experience of the nurses. The highest number was 1-5 years experience (32.8, 132 nurses), then 6-10 years (30.5, 123 nurses), over 10 years (28.5, 115 nurses) and less than one year (8.2, 33 nurses). The majority of nurses (76.9%, n=310) had received formal training in ABG, while 23.1% (n=93) had not.
Figure 4 shows the latest date in which the 310 trained nurses received the ABG training.
The majority (67.1%, 208 nurses) of them were trained more than one year ago, 17.4 percent (54 nurses) trained 6-12 months ago, and 15.5 percent (48 nurses) had been trained within the past six months.
Figure 3. Experience of the participants
Figure 4. Training Recency of the participants
To Test the Acid-Base Knowledge Level of Nurses
Section 10 items assessed the knowledge that nurses had on the acid-base balance. Descriptive Statistics of Knowledge and Skills Scores were shown in Table 2.
Variable Mean SD Median Min Max Knowledge Score (out of 10) 9.78 0.58 10 8 10 Skills Score (out of 60) 53.8 6.2 56 38 60 Knowledge Score Distribution (Score) Frequency (n)
Percentage (%)
8 16 4 9 26 6.5 10 361 89.5 Skills Score Distribution (Score Range)
Frequency (n)
Percentage (%) 35–40 12 3 41–45 42 10.4 46–50 84 20.8 51–55 70 17.4 Table 2. Descriptive Statistics and Distribution of Knowledge and Skills Scores (N=403).
The mean score was 9.78 of 10 (SD=0.58), which means that there is high knowledge.
Very high percentage (89.5%) (n=361) scored 10, which depicts a very good understanding of acid-base concepts.
To Evaluate Self-Reported Clinical Monitoring Skills with the Nurses
Clinical monitoring skills were measured using section C (12 items) on a 5-point Likert scale. The average was 53.8 of 60 (SD = 6.2), which was high self-report competence. The highest range (56-60) had almost half (48.4) of the total scores, which reflects good monitoring skills.
To Establish the Relationship between the Acid-Base Knowledge and Clinical Monitoring Skills
Pearson’s correlation analysis was conducted to examine the relationship between knowledge scores (Section B) and skills scores (Section C). As shown in Table 2, a statistically significant, positive, and strong correlation was observed. Nurses with higher knowledge scores also demonstrated higher clinical monitoring skills scores.
Variable Pair Correlation Coefficient (r) 95% CI p-value Knowledge Score & Skills Score 0.82 [0.78, 0.86] < 0.001 Note: Pearson correlation assumptions were checked before analysis. The Shapiro-Wilk test was used to test normality (p > 0.05). Scatterplots were used to determine the linearity and the relationship was linear. No extreme outliers were observed (no values more than ±3 standard deviations of the mean) Table 3. Pearson coefficient of Knowledge and Skills Scores.
To Compare Clinical Monitoring Skills in the various demographic and professional groups
Comparison by Gender
The difference between the scores of the skills of male and female nurses did not show significant differences (p=0.156).
Comparison of Qualification
Higher level qualifications related with better clinical monitoring skills.
Comparison by Experience
As one becomes more skilled the skills increase. The distance between all the experience groups is significant (p < 0.01).
Comparison of ABG Training status
Table 3 summarizes differences in clinical monitoring skills scores according to demographic and professional variables, as assessed through t‑tests and one‑way ANOVA.
ABG trained nurses scored significantly higher (p < 0.001). Central Finding: The greater the level of qualifying is, the greater the monitoring skills. Post-hoc tests indicate that the MSN nurses had scores that were high as compared to the diploma nurses (p = 0.006).
Variable Categories N Mean Skills Score SD Statistic p-value Gender Male 207 54.2 6 t = 1.42 0.156 Female 196 53.4 6.4 Qualification Diploma 253 53.2 6.4 F = 4.89 0.008 BSN 117 54.6 5.8 MSN 33 56.1 5.2 Experience < 1 year 33 44.8 4.2 F = 48.2 < 0.001 1-5 years 132 51.2 5.1 6-10 years 123 55.4 5.3 > 10 years 115 57.8 4.6 ABG Training Yes 310 55.9 5.1 t = 12.4 < 0.001 No 93 46.8 5.5 Table.4 Comparison of Clinical Monitoring Skills by Demographic and Professional Groups
Knowledge-to-Practice Application
To assess the application of knowledge into clinical practice, Section D comprised five statements rated on a 5‑point Likert scale. Table 5 summarizes nurses’ responses in terms of mean scores and standard deviations.
Statement Mean SD D1: Theoretical knowledge helps recognize problems earlier 4.65 0.48 D2: Consciously apply acid-base concepts when assessing patients 4.42 0.69 D3: There is a gap between class learning and clinical practice 2.82 0.87 D4: Confident connecting lab results to physical assessment 4.38 0.71 D5: Continuing education would improve monitoring skills 4.92 0.27 Table 5. Knowledge-to-Practice Application Statements.
Key Findings:
- 8 percent said they agreed or strongly agreed that theoretical knowledge helps in early problem recognition.
- 1% said that they are aware of using acid-base concepts.
- Perceived gap between learning and practice in classrooms was seen in only 18.6% of the people.
- 5 percent strongly agreed that the continuation of the education would advance monitoring skills.
To Determine Bars to Counterproductive Nursing Care in the Surveillance of Acid-Base Imbalances in the Patient
Participants were asked to identify perceived barriers to effective acid–base monitoring. Table 6 summarizes the frequency and percentage distribution of the reported barriers, with heavy workload identified as the most common impediment.
Barrier Frequency (n) Percentage (%) Heavy workload / too many patients 218 54.1 Lack of time 145 36 Lack of confidence in interpreting results 32 7.9 Insufficient training 28 6.9 Limited access to ABG results 12 3 Lack of experienced staff to consult 8 2 None 18 4.5 Table 6. Hurdles to Successful Surveillance.
The most frequently reported barriers to effective acid–base monitoring were heavy workload and lack of time.
Regression Analysis
Simple Linear Regression
Simple linear regression was used to determine the prediction of clinical monitoring skills using acid-base knowledge scores.
Clinical monitoring skills were explained by knowledge score 0.52. The skills score increased by 8.82 points with each one-point increment in the knowledge score.
Model summary R2 F-statistic df numerator df denominator p-value 0.52 441.0 1 401 <0.001 Predictor Β SE t-statistic p-value 95% CI Knowledge Score 8.82 0.42 21 < 0.001 [7.99, 9.65] Table 7. Simple Linear Regression Analysis.
Multiple Linear Regression
To identify clinical monitoring skills as related to the knowledge score, qualification, experience, and status of the ABG training, the multiple linear regression was performed.
Model summary R2 F-statistic df numerator df denominator p-value 0.81 425.6 4 398 <0.001 Predictor β SE t-statistic p-value 95% CI Knowledge Score 7.45 0.38 19.6 < 0.001 [6.70, 8.20] Qualification 1.12 0.28 4 < 0.001 [0.57, 1.67] Experience 1.89 0.22 8.6 < 0.001 [1.46, 2.32] ABG Training (Yes) 4.32 0.48 9 < 0.001 [3.38, 5.26] Table 8. Multiple Linear Regression Analysis.
The entire model explained 81 percent of the variance in clinical monitoring skills. Knowledge score was the most significant one with experience coming in next, followed by ABG training status and qualification.
Critical Discussion of the Results
The researchers concluded that the majority of the nurses had a high level of knowledge and a good clinical monitoring ability associated with acid-base balance. However, on closer examination of the individual survey questions and subgroup analyses, there is a more detailed image. The nurses were not equally effective in exhibiting flawless competency in all the clinical monitoring activities. The level of knowledge was very high and K=89.5 and 10 is the highest possible mark, which means that the theoretical basis is strong. Contrastingly, the variation in terms of skills scores was less: 48.4 percent were in the upper tack (56-60), 20.8 percent were in the mid-range (46-50). This implies that academic knowledge may not necessarily become an ideal clinical practice. Self-reported skills differed even in the case of nurses who scored perfectly in terms of knowledge, which suggests that other variables have an impact on performance. Demographic analysis of skills scores was important in identifying some trends. Nurses who are less than one year experienced the lowest average skills score (44.8/60), and those who are over ten years experienced the highest (57.8/60). The gradual change highlights the importance of experiential learning but also brings up the issue of the willingness of novice nurses to be able to monitor the patients in isolation. The significant difference in means of 13 between novice and experienced nurses indicates that the development of skills with the help of structured mentorship might be faster. Formal ABG training was associated with much higher scores in skills (55.9 vs. 46.8, p 0.001). Nevertheless, 23.1 percent of nurses were not sufficiently trained on the use of ABG, which stands as a serious gap in the workforce susceptibility. On the Knowledge-to-Practice Application section, 95.8 percent of the nurses concurred that theoretical knowledge aids in diagnosing the problems at an earlier stage, but only 90.1 percent agreed to be conscious in applying the concepts of acid-base when evaluating patients. The 5.7 percent disparity is the indication of a small yet significant gap between the recognition and the regular use. Moreover, 18.6 per cent of nurses indicated that they felt disconnected between their classroom and clinical practice indicating that theory-practice gap was still present in some. The barriers analysis indicated that the most frequent barriers are heavy work load (54.1) and time (36.0). Such systemic conditions can hamper the implementation of competencies despite nurses being informed. The quality of monitoring is also a problem due to the high incidence of heavy workload which was the main area of concern regarding patient to nurse ratio. The regression models (R2 = 0.52; 0.81) and the correlation analysis (r = 0.82) indicate that there is a medium-high correlation between knowledge and skills. But the complete model predicts 81 per cent of the variance leaving 19 per cent unaccounted. The remaining variance can be due to factors that cannot be measured including personal motivation, cognitive burden, work culture, or access to mentors. When most nurses were found not lacking in confidence in their skills, quite a significant number showed concerns. Obstacles connected to the absence of confidence (7.9%), as well as the lack of training (6.9%), indicate that the knowledge does not necessarily ensure the lack of confidence or the positive attitude. These results highlight the importance of developing confidence-building techniques and workload management of nursing education and practice. In general, the findings suggest that even though the nurses are well-equipped in terms of acid-base knowledge, the usage of that knowledge in clinical monitoring is experience-dependent, training-dependent, as well as systemic. The educational interventions must be oriented not just on acquiring the knowledge, but also confidence development, exposure to practical experience in simulation and training exercises, and institutional concerns (workload, time limitations) to facilitate bedside implementation.
DISCUSSION
This paper discussed the relationship between acid-base knowledge and clinical monitoring competencies among nurses in Northeastern Pakistan. The results showed that the level of acid-base knowledge (mean = 9.78/10, SD = 0.58) and self-reported clinical monitoring skills (mean = 53.8/60, SD = 6.2) was high and there was a positive correlation between the two variables (r = 0.82, p < 0.001). Such results imply that an excellent theoretical basis in acid-base physiology is inextricably linked with improved clinical monitoring among nurses. The level of knowledge in this study (89.5% stating 10/10) is in line with Dakic et al. [1] in which interactions teaching method enhanced the students’ knowledge of acid-base physiology. On the same note, Brown et al. [2] observed that nursing students excel on written exams, but it is difficult to exercise the knowledge in clinical practice, and this correlates with the current research in which some respondents show a theory-practice gap. The correlation between knowledge and skills (r = 0.82) is strong, which is similar to those of international studies. In their study, Nassar and Schmidt [15] and Prasad et al. [18] noted that proper interpretation of ABG needs a sound theoretical foundation, which explains why we have found that knowledgeable nurses report higher monitoring skills. Moreover, Endacott et al. [5] discovered that successful monitoring involves the combination of technical skills and clinical reasoning, which is also consistent with the high level of skills given by our participants.
No differences in clinical monitoring skills were significant between the genders (p = 0.156), which concurs with Zhang et al. [10], who had no gender differences in metabolic abnormalities in nurses. But, higher qualified nurses showed much better monitoring skills (F = 4.89, p = 0.008), with the MSN-prepared nurses scoring higher than the diploma nurses (p = 0.006). This correlates with the study of Baiee and Ali [19] who observed that increased education is related to enhanced knowledge and skills. Clinical monitoring skills had a strong association with experience (F = 48.2, p < 0.001), which is consistent with Endacott et al. [5], who have found that advanced nursing competencies are acquired with time through clinical exposure. In the same vein, formal ABG training was also connected with much higher skills scores (t = 12.4, p < 0.001), which confirms the results of Fujimoto et al. [16], Zeserson et al. [17], and Prasad et al. [18] about the importance of structured training. Most of the nurses (95.8% said that theoretical knowledge would help them identify problems early and 96.5% said strongly that further education would enhance their monitoring abilities. These results indicate that continuous professional growth is necessary, as it is also stressed by Baiee and Ali [19]. The most common obstacles to effective monitoring were heavy workload (54.1) and lack of time (36.0). These findings are in line with those of Zhang et al. [10] who found that workload and shift-related stress are significant determinants of nurse behavior. These obstacles are especially applicable to the Pakistani environment, where the nurse-to-patient ratios are difficult [20]. The regression analysis showed that the knowledge score alone was sufficient to explain 52 percent of the variance in clinical monitoring skills (R2 = 0.52), whereas the complete model that contains knowledge, qualification, experience, and ABG training was sufficient to explain 81 percent (R2 = 0.81). Knowledge score was the strongest predictor (β = 7.45, p < 0.001), followed by experience (β = 1.89, p < 0.001), ABG training (β = 4.32, p < 0.001), and qualification (β = 1.12, p < 0.001).
CONCLUSION
This paper has shown that there is a close relationship between acid-base education and clinical monitoring competency among nurses in Northeastern Pakistan. These findings revealed that nurses had good acid-base knowledge (mean = 9.78/10) and clinical monitoring skills (mean = 53.8/60), and the two variables had a strong positive correlation (r = 0.82, p < 0.001). Increased qualification, experience, and formal training on ABG were linked to better monitoring skills. The barriers to effective monitoring were most often reported, 54.1%, and lack of time (36.0%).
Regression analysis found the best predictor of clinical monitoring skills is the acid-base knowledge, which explains 52% of variance. The entire model comprising experience, qualification, and training in ABG explained 81 percent of the variance. These results suggest that theoretical understanding of acid-base balance is one of the factors correlated with clinical monitoring competency in nurses.
Recommendation
For Nursing Education
The teaching of acid-base contents should be supported by interactive and case-based methods through nursing education programs to increase knowledge and memory [1,2]. The use of simulation-based training in the curricula is necessary to provide the students with a hands-on experience of ABG interpretation and clinical monitoring practice [5,7].
For Clinical Practice
A formal training process of practicing nurses should be developed by healthcare institutions since formal training was significantly associated with a higher level of monitoring skills [15,17,18]. Continuous education programs must be carried out on a regular basis to sustain and improve clinical competencies, particularly in acid -base management [19].
In the case of Healthcare Institutions
Workload and time issues should be handled with institutional leadership by maximizing the number of nurses per patient, since the issue of workload per patient is the most prevalent one that is reported as an obstacle to effective monitoring [10,20]. ABG results should be made available in a timely manner to assist in clinical decision-making and to enable some timely interventions [12,16].
For Future Research
Objective data in clinical monitoring skills including direct observation or simulation-based measures should be used in future studies to improve self-reported data [7]. Multi-centered research in the various areas of Pakistan is required to enhance the overall external validity of the results [20].
Limitations
This study has several limitations. To begin with, convenience sampling will restrict the generalizability of the findings to the whole population of nurses in Pakistan. Second, the skills reported by the self might not be the ones that are objective in clinical performance, because perceptions are not necessarily associated with competency. Third, the study design is cross-sectional and therefore no causal inferences can be made; despite a strong association, it is not possible to imply causality. Fourth, the research was only done in a single region (Northeastern Pakistan) and this could not be generalized to other geographical regions. Fifth, causal relationships cannot be drawn because of the cross-sectional design. The interrelationships mentioned in this research are not causal but correlational. These limitations could be overcome by future studies based on probability sampling, objective competency tests, and multi-center studies.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Local ethics Committee Approval
The study was conducted in accordance with international ethical guidelines of conducting research with human participants. This was done to safeguard the rights, safety and well-being of participants by ensuring we had ethical approval prior to data collection. The study plan was discussed and reviewed by the Institutional Review Board (IRB) of Abdul Wali Khan University, Mardan.
- IRB Title: The Institutional Review Board, Abdul Wali Khan University Mardan
- IRB Number: IRB/2025/Acid-Base/Biochem/Nursing/0011.
- Date of Approval: November 21, 2025
The involvement was completely voluntary. The purpose, methodology, possible benefits and the right of the registered nurses to discontinue the study were explained to them without any penalty to their job or personal status. All the participants signed consent papers prior to data collection.
No personal identifiers were obtained, which guaranteed confidentiality and anonymity. Questionnaires were coded in numbers, and all the data were stored safely, in a place where only the research team can access. The data was only utilized in terms of academic and research purposes. The research did not produce any physical, mental, or professional damage. It was not a sensitive topic, and the participants were not required to answer any question that would not be comfortable to them, which was connected to the topic (acid-base understanding and clinical monitoring skills).
Conflict of interest
The authors do not claim any conflicts of interest.
Authors’ contribution
The conceptualization was done by Abdur Rahman and Jihad Hussain and the study design. The methodology and the instruments were developed by Shams Ul Haq, Zohaib Hussain, and Muhammad Shayan. Shams Ul Haq, Mah Noor Mumtaz and Wajid Hussain organized data collection and fieldwork. Zohaib Hussain, Mahnoor Ali and Abdur Rahman took part in the data analysis and interpretation. Muhammad Sohrab Khan, Muhammad Ishaq and Hamza Khan helped in the literature review, drafting of the manuscript and initial validation of data. Muhammad Shayan helped in logistics of the fieldwork and data entry. Abdur Rahman led the research, optimized the methodology, and supervised the writing, revision, and final approval of the manuscript.
The final version of the manuscript was approved by all the authors.
Additional Author Information
The email addresses and ORCID identifiers of the authors are reported below.
Muhammad Sohrab Khan: email: sohrab_dr2002@hotmail.com; ORCID: Not available
Jihad Hussain: email: legendenterprise094@gmail.com; ORCID: 0009-0009-4914-9045
Muhammad Ishaq: email: mishaqlkr0349@gmail.com; ORCID: 0009-0006-5906-1854
Shams Ul Haq: email: qaris729@gmail.com; ORCID: 0009-0004-6055-466X
Hamza Khan: email: khankhan983933@gmail.com; ORCID: 0009-0003-7618-7967
Muhammad Shayan: email: shayanmuhammad847@gmail.com; ORCID: 0009-0007-3498-828X
Zohaib Hussain: email: zk4542471@gmail.com; ORCID: Not available
Mah Noor Mumtaz: email: mahnoor@awkum.edu.pk; ORCID: 0009-0004-3699-7559
Wajid Hussain: email: wajid.awkum@gmail.com; ORCID: 0009-0007-4307-4287
Abdur Rahman: email: abdurrahman888889998@gmail.com; ORCID: 0009-0008-2170-146X
Mahnoor Ali: email: mahnorralimdcat2022@gmail.com; ORCID: 0009-0003-6475-2607
Acknowledgements
The authors are grateful to all registered nurses who took part in this study and also recognize the assistance of nursing supervisors, head nurses, and clinical coordinators that were used during recruitment.
Application of Artificial Intelligence Tools
No artificial intelligence software was applied other than a regular grammar and spell.
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THE IMPACT OF AN INTERACTIVE MODULE–BASED REFLECTIVE FLIPPED CLASSROOM ON SELF-EFFICACY AND REFLECTIVE THINKING IN SPIRITUAL CARE EDUCATION FOR NURSING STUDENTS IN INDONESIA: A QUASI-EXPERIMENTAL STUDY
Inggriane Puspita Dewi 1*, Popy Irawati 2, Sharifah Shafinaz Sh Abdullah 3,
Soviaturohmah Nur Rizky 4, Resti Febrianti 4, Santy Sanusi 1
- Department of Nursing, Faculty of Health Sciences, Universitas Aisyiyah, Bandung, West Java, Indonesia.
- Department of Nursing, Faculty of Health Sciences, Universitas Muhammadiyah, Tangerang, West Java, Indonesia.
- Centre for Nursing Studies, Faculty of Health Sciences, UiTM Selangor, Puncak Alam, Selangor, Malaysia.
- Nursing Department, Faculty of Health Sciences, Universitas Aisyiyah, Bandung, West Java, Indonesia.
* Corresponding author: Inggriane Puspita Dewi, Department of Nursing, Universitas Aisyiyah, Bandung, West Java, Indonesia. E-mail: inggriane.puspita@unisa-bandung.ac.id
Cite this article
ABSTRACT
Introduction: Spiritual care is essential in holistic nursing; however, nursing students often feel less confident and reflective in providing it. Innovative teaching methods combining active learning and reflection are needed to fill this gap.
Objective: This study evaluated the impact of reflective, flipped-classroom spiritual care training on students' self-efficacy and reflective thinking.
Methods: A quasi-experimental pretest–posttest design with a control group was conducted among 82 undergraduate nursing students from two universities in Indonesia. Participants were allocated to an intervention group (n = 41) or a control group (n = 41) based on existing class enrollment. The intervention consisted of a 16-week reflective flipped classroom supported by an interactive e-module, while the control group received conventional lecture-based instruction. Self-efficacy and reflective thinking were measured at baseline and post-intervention. Data normality was assessed using the Shapiro–Wilk test. Within-group differences were analyzed using the Wilcoxon signed-rank test or paired sample t-test as appropriate, and between-group differences were examined using the Mann–Whitney U test.
Results: Within-group analyses showed statistically significant improvements in self-efficacy and reflective thinking in the intervention group (p < 0.001). In the control group, changes in reflective thinking were not statistically significant (p = 0.062). Between-group post-test comparisons demonstrated significantly higher self-efficacy and reflective thinking scores in the intervention group than in the control group (p < 0.001), with a large effect (r = 0.67 for self-efficacy and r = 0.61 for reflective thinking).
Conclusion: The findings indicate that a reflective flipped classroom approach is associated with higher self-efficacy and reflective thinking among nursing students in spiritual care education. While causal conclusions cannot be drawn, the results support the educational value of reflective and interactive learning strategies in undergraduate nursing curricula.
Keywords: spiritual care, flipped classroom, reflective learning, self-efficacy, nursing education.
INTRODUCTION
Spirituality is increasingly recognized as a core component of holistic nursing, encompassing individuals' search for meaning, purpose, connection, and transcendence amid health, illness, and suffering [1,2]. In clinical practice, spiritual care plays a crucial role in supporting patients' emotional well-being, coping processes, and psychological adjustment, particularly among individuals facing chronic illness, serious health conditions, and end-of-life situations [3,4].
A growing body of empirical evidence indicates that spiritual care interventions are associated with positive patient outcomes across diverse healthcare settings. Previous studies have reported that spiritual care may contribute to reducing anxiety, enhancing emotional regulation, improving coping strategies, and a greater sense of meaning and connectedness among patients experiencing vulnerability or existential distress [5,6]. These findings underscore nurses' professional responsibility to competently assess and address patients' spiritual needs as an integral part of person-centered care. Despite its recognized importance, spiritual care remains one of the areas in which nurses and nursing students report the lowest levels of confidence. Numerous studies have shown that nurses often feel uncertain, uncomfortable, or inadequately prepared to engage in spiritual care, even when they acknowledge its relevance to quality nursing practice [2,7,8]. This discrepancy suggests a persistent gap between professional expectations and cultural clinical practice. One contributing factor to this gap lies in undergraduate nursing education. Although spirituality is frequently included in nursing curricula, it is often addressed at a conceptual or theoretical level, with limited opportunities for experiential learning, structured reflection, and skill-based application [9,10]. As a result, nursing students may develop theoretical awareness of spiritual care without sufficient confidence or readiness to engage in spiritual conversations and interventions during clinical encounters [11,12]. Research focusing on nursing students highlights particular challenges related to self-efficacy and reflective capacity in spiritual care. Self-efficacy, defined as an individual's belief in their ability to perform specific tasks [9,10], plays a key role in translating knowledge into action. Students with low self-efficacy may avoid initiating spiritual care interactions, even when they possess adequate theoretical understanding [13,14]. In parallel, reflective thinking is essential for effective spiritual care, as it enables nurses to critically examine their personal values, emotional responses, and professional responsibilities when addressing patients' existential concerns [15,16]. However, reflective practice is not consistently embedded in nursing education. Previous studies have reported that nursing curricula often lack structured reflective activities, standardized guidance, and intentional pedagogical planning to support the development of reflective thinking alongside clinical competence [17,18]. This limitation may hinder students' ability to integrate spiritual care knowledge with self-awareness and ethical sensitivity. To address these educational challenges, active and student-centered learning strategies that intentionally integrate reflection are increasingly recommended. The flipped classroom model, which shifts content delivery to pre-class learning and utilizes in-class time for higher-order cognitive activities, has gained recognition as an effective pedagogical approach in nursing education [19,20]. Additionally, flipped classrooms support self-regulated learning and increase student confidence, which are linked to the development of self-efficacy [21,22]. However, the use of flipped classroom strategies in spiritual care education remains limited, particularly when reflective learning is not intentionally integrated into the teaching approach. Within a flipped classroom framework, interactive learning modules can function as structured pre-class resources that integrate content, reflection, and formative feedback. In this study, an interactive module is conceptualized as a structured instructional unit that combines case-based scenarios, guided reflective questions, and multimedia content to support self-directed and meaningful learning [4,19,23]. In a flipped classroom, it serves as a pre-class tool that prepares students for higher-order activities during in-class sessions, which focus on discussion, reflection, and the application of spiritual care concepts in simulated or case-based contexts [24].
The interactive and reflective nature of the module aligns with key outcomes in spiritual care education. Active engagement with realistic scenarios and reflection helps develop self-efficacy and reflective thinking, especially in emotional sensitivity and existential care [16,25]. Despite existing spiritual care competency frameworks, guidance on pedagogical methods that foster reflective thinking and self-efficacy through innovative, student-centered learning is limited.
A significant gap remains in nursing education regarding the effective implementation of spiritual care competencies through integrated, reflective, and interactive flipped classroom approaches.
Objective
This study investigates the impact of a reflective flipped classroom, supplemented by an interactive module, on the self-efficacy and reflective thinking of undergraduate nursing students in spiritual care. It is expected that students engaging with this innovative learning model will show notably greater self-efficacy and reflective thinking than those taught through traditional methods approaches.
MATERIALS AND METHODS
Design
A quasi-experimental pre-post-test design with a control group was used to examine differences in self-efficacy and reflective thinking between students who participated in a reflective flipped classroom intervention and those who received traditional instruction.
strong>Participants and Setting
The study population comprised undergraduate nursing students in Indonesia. The sample size calculation was performed using G*Power version 3.1, based on the Wilcoxon–Mann–Whitney test for two independent groups, with a significance level of 0.05 and a statistical power of 80% [26,27]. This choice was made because the outcome variables were expected to be analyzed using a nonparametric approach if the assumption of normality was violated. A moderate effect size (d = 0.60) was selected based on previous meta-analyses of flipped classroom interventions in nursing education, which report medium to large effects on educational outcomes [26,27]. This conservative estimate was chosen to avoid overestimating intervention effects in applied educational settings.
Based on this calculation, a minimum of 37 participants per group was required. To account for potential participant attrition, an additional 10% was added to the total sample size, resulting in a final sample of 82 participants (41 per group).
A purposive sampling technique was employed. The inclusion criteria were undergraduate nursing students in their third year of academic study who were actively enrolled and willing to participate in the study. The exclusion criteria included undergraduate nursing students who were on academic leave during the data collection period.
The study was conducted at two universities in West Java that had supportive curricula, classrooms, and labs for learning activities and data collection processes.
Group Allocation and Baseline Comparability
Participants were allocated to either the intervention or control group based on their existing class assignments at each institution to minimize contamination between groups. Random assignment was not feasible due to academic scheduling constraints.
To reduce selection bias, both groups were drawn from the same academic year and comparable institutional settings. Baseline comparability was assessed using demographic characteristics (age, gender, and religion), which showed similar distributions between groups (Table 1), supporting demographic equivalence at study entry. However, no baseline psychometric measurements were collected for the outcome variables.
Assessments and Measures
Post-intervention data on self-efficacy and reflective thinking were collected from the intervention group after the instructional intervention concluded. Control group data were collected simultaneously at the corresponding time point in separate classrooms to ensure temporal equivalence and minimize cross-group contamination. The estimated time to complete the questionnaires was 25-30 minutes.
The self-efficacy instrument was adapted from Bandura's (1997) [25] General Self-Efficacy, and reflective thinking was measured using the Level of Reflective Thinking Questionnaire developed by Kember et al. (2000) [28]. Prior to the main study, the translated instruments were tested for validity with a pilot sample of 30 undergraduate nursing students. The instruments were translated into Indonesian using a forward–back translation procedure, followed by expert review to ensure semantic and conceptual equivalence. A pilot test was conducted with 30 undergraduate nursing students to assess clarity and cultural appropriateness.
Reliability testing demonstrated high internal consistency, with Cronbach's alpha coefficients of 0.882 for self-efficacy and 0.984 for reflective thinking. While these values indicate strong reliability, the very high alpha for reflective thinking may also suggest potential item redundancy, which should be considered when interpreting results.
Each questionnaire consisted of 20 items, both the reflective thinking and self-efficacy instruments used the same scoring classification, categorizing scores as very (1) very poor [0,20[, (2) poor [20,40[, (3) average [40,60[, (4) good [60,80[, and (5) excellent (≥80).
Intervention Procedures
The intervention was developed using a constructive alignment framework to ensure coherence among course learning outcomes (CLOs), learning activities, and assessment strategies. It was grounded in the principles of flipped classroom pedagogy and reflective learning. The total workload was equivalent to 2 academic credit units (approximately 58 hours), delivered over 16 weeks with an average of 4 hours of learning activities per week [9]. The full set of intervention procedures is outlined in Table 1.
Phase Timing Learning Activities Learning Materials / Tools Purpose Preparation Phase Before semester 1. Development of an interactive e-module 2. Alignment of course learning outcomes, activities, and assessment
3. Facilitator briefing (lecturers, chaplain, palliative nurses)
4. Learning management system (LMS) setup
1. Interactive e-module 2. Semester learning plan
3. Google Classroom
Ensure instructional consistency and constructive alignment Baseline Assessment (Pre-Test) Week 0 (before intervention) 1. Orientation session and informed consent 2. Administration of baseline questionnaires to both groups
1. Self-efficacy (pre-test) 2. Reflective thinking (pre-test)
Assess baseline equivalence between intervention and control groups Pre-Class Learning (Flipped Component) Weekly (≈ 2 hours/week) 1. Independent study using an interactive module 2. Viewing instructional videos
3. Analysis of case-based spiritual care scenarios
4. Completion of guided reflective questions
5. Formative quizzes
1. Interactive e-module 2. Instructional videos
3. Case scenarios
4. Online quizzes
Build foundational knowledge and support self-directed learning In-Class Learning (Reflective & Active Learning) Weekly (face-to-face / synchronous sessions) 1. Facilitated case-based group discussions 2. Guided reflective dialogue
3. Role play and communication skills practice
4. Spiritual assessment exercises
5. Practice of religion-based spiritual care (Islamic context)
1. Case discussion guides 2. Reflection prompts
3. Skills demonstration tools
Apply theoretical knowledge, enhance reflective thinking, and strengthen self-efficacy Reflection and Feedback Throughout semester 1. Submission of structured reflective journals 2. Facilitator feedback on reflection and participation
3. Ongoing formative assessment
1. Reflective journal templates 2. LMS feedback features
Deepen self-awareness and reinforce reflective learning Post-Intervention Assessment End of semester 1. Completion of the self-efficacy questionnaire 2. Completion of the reflective thinking questionnaire
1. Self-efficacy scale 2. Reflective thinking questionnaire
Evaluate post-intervention outcomes and compare groups Control Group (Comparison) Throughout semester 1. Traditional lecture-based instruction 2. Classroom discussion without structured reflection or flipped classroom elements
1. Lecture materials 2. Standard classroom resources
Provide a comparison condition without reflective flipped learning Table 1. Intervention Procedures.
Data Collection and Statistical Analysis
Descriptive statistics, including means, standard deviations, frequencies, and percentages, were used to summarize participants' demographic characteristics and study variables. Instrument reliability was assessed using Cronbach's alpha coefficients.
The Shapiro–Wilk test was used to assess the normality of the distributions of self-efficacy and reflective thinking scores in both the intervention and control groups. In particular, a p-value greater than 0.05 indicates that the null hypothesis of normality cannot be rejected.
To examine within-group differences between pre-test and post-test scores in both the intervention and control groups, paired-sample t-tests were used to assess differences between means. When data were not normally distributed, the non-parametric Wilcoxon signed-rank test was applied as an alternative. To compare between-group differences in outcome scores, independent-sample t-tests were used. In cases of non-normal distribution, the Mann–Whitney U test was employed as a non-parametric alternative.
Effect sizes for the Mann–Whitney U test were calculated using the rank-biserial correlation (r) derived from the standardized Z value to estimate the magnitude of group differences and were interpreted according to established criteria. A two-tailed p-value < 0.05 was considered statistically significant.
All statistical analyses were performed using SPSS software (version 27).
RESULTS
Participants Characteristic
In Table 2 we have reported the main characteristics of our sample of undergraduate nursing students. Participants in both groups were predominantly in early adulthood. The intervention group had a mean age of 21.51 years (median = 21; range, 19–28), while the control group had a mean age of 20.54 years (median = 20; range, 19–26). In the intervention group, the highest proportion of participants was aged 21 years (29%), whereas in the control group, the majority of participants were aged 20 years (51%). Female participants constituted the majority in both groups. In the intervention group, 71% of participants were female, and 29% were male. Similarly, the control group consisted of 85% female and 15% male participants. All participants in the Islamic religion.
Comparable age and gender characteristics across groups indicate baseline demographic homogeneity, thereby supporting the internal validity of the quasi-experimental design.
Characteristics Intervention Group Control Group f % f % Age (years) 19 4 10 5 12 20 7 17 21 51 21 12 29 10 24 22 9 22 2 5 23 5 12 1 2 24 2 5 1 2 25 1 2 0 0 26 0 0 1 2 28 1 2 0 0 Gender Male 12 29 6 15 Female 29 71 35 85 Religion Islam 41 100 41 100 Christianity 0 0 0 0 Buddhist 0 0 0 0 Hinduism 0 0 0 0 Table 2. Participants Characteristics
In the intervention group, the Shapiro–Wilk test showed that both pre-test and post-test scores for self-efficacy (p < 0.0001) and reflective thinking (pre-test p = 0.001; post-test p < 0.0001) were not normally distributed. Therefore, non-parametric statistical tests were considered appropriate for within-group analyses in the intervention group. In contrast, in the control group, the Shapiro–Wilk test indicated that pre-test and post-test scores for self-efficacy (pre-test p = 0.214; post-test p = 0.149) and reflective thinking (pre-test p = 0.123; post-test p = 0.057) were normally distributed (p > 0.05). Accordingly, parametric tests were applied for within-group analyses in the control group (Table 3).
Intervention Group Control point n p-value Self-Efficacy Pre-test 41 < 0.001 Post-test 41 < 0.001 Reflective Thinking Pre-test 41 0.001 Post-test 41 < 0.001 Control Group Control point n p-value Self-Efficacy Pre-test 41 0.214 Post-test 41 0.149 Reflective Thinking Pre-test 41 0.123 Post-test 41 0.057 Table 3. The Shapiro–Wilk Normality Test
Table 4 presents within-group comparisons of pre- and post-test scores for self-efficacy and reflective thinking in the intervention and control groups.
Variables Group Pre-Test (Mean ± SD)
Post-Test (Mean ± SD)
p-value Self-Efficacy Intervention 82.10 ± 9.11 90.32 ± 9.59 < 0.001 a Control 73.49 ± 8.73 80.15 ± 11.66 < 0.001 b Reflective Thinking Intervention 80.54 ± 9.19 87.10 ± 9.97 < 0.001 a Control 73.05 ± 8.19 77.49 ± 12.00 0.062 b Note: a = (Wilcoxon Signed-rank); b = (Paired Sample T-Test)
Table 4. Pre-Test and Post-Test Comparison within Intervention and Control Groups
In the intervention group, statistically significant differences were observed between pre- and post-test scores for both self-efficacy and reflective thinking (p < 0.001), indicating higher post-test scores than at baseline. In contrast, within the control group, no statistically significant differences were found between pre- and post-test scores for self-efficacy or reflective thinking (p = 0.062). These findings suggest different patterns of change over time between the intervention and control groups.
Table 5 further supports these findings through inferential analysis. Nursing students who participated in the reflective flipped classroom showed notably higher self-efficacy scores (mean = 90.32 ± 9.59) than those in the control group (mean = 80.15 ± 11.66), with a difference of 10.17 points (p < 0.001). Similarly, reflective thinking scores were substantially higher in the intervention group (87.10 ± 9.97) than in the control group (77.49 ± 12.00), with a difference of 9.61 points (p < 0.001). These significant and statistically strong differences suggest a meaningful educational impact rather than a minor effect improvement.
variables n mean±SD mean difference (IC 95%)
p-value Z Effect size Self-efficacy (intervention group) 41 90.32±9.59 10.17 <0.001 6.10 r = 0.67 large effect
Self-efficacy (control group) 41 80.15±11.66 Reflective thinking (intervention group) 41 87.10±9.97 9.61 <0.001 5.65 r = 0.61 large effect
Reflective thinking (control group) 41 77.49±12.00 Table 5. Comparison of Post-Test Self-Efficacy and Critical Reflection Scores Between Intervention and Control Groups (Mann–Whitney U Test).
Comparison of Post-Test Self-Efficacy and Critical Reflection Scores Between Intervention and Control Groups (Mann–Whitney U Test)
The analysis of effect sizes showed a significant educational benefit from the reflective flipped classroom approach to spiritual care education. The effect sizes were large for both self-efficacy (r = 0.67) and reflective thinking (r = 0.61), indicating substantial differences between the intervention and control groups. These results demonstrate that the improvements are both statistically significant and educationally important, indicating a strong enhancement of effective and cognitive skills among nursing students. Reporting effect sizes along with p-values is recommended to provide information on the practical significance of the findings and to help compare results across studies [29].
DISCUSSION
This study examined changes in self-efficacy and reflective thinking among nursing students participating in an interactive, module–based reflective flipped classroom compared with those receiving traditional instruction. By incorporating both within-group and between-group analyses, the findings provide a more nuanced understanding of how students' learning outcomes evolved over the intervention period.
Within-group analyses showed that students in the intervention group experienced statistically significant improvements in both self-efficacy and reflective thinking from pre-test to post-test. These findings suggest that participation in a structured learning environment combining flipped classroom strategies and guided reflection was associated with higher post-intervention scores. Such outcomes align with theoretical perspectives that emphasize the role of active engagement and reflective processes in strengthening learners' confidence and cognitive development [25], [28]. Reflective learning activities, such as guided journals and case-based discussions, may help students make sense of complex learning experiences and integrate theoretical knowledge with professional values [15,16]. In contrast, in the control group, only self-efficacy showed a statistically significant pre–post change, while reflective thinking did not. This pattern suggests that conventional lecture-based instruction may support certain aspects of learning, such as perceived confidence, but may be less effective in fostering deeper reflective capacities without explicit reflective structures. Previous studies have similarly reported that the absence of intentional reflective pedagogies can limit students' development of reflective thinking skills, particularly in professional nursing education contexts [29,30].
Between-group comparisons at post-test further indicated that students in the intervention group reported higher levels of self-efficacy and reflective thinking than those in the control group, with statistically significant differences for both outcomes. These findings align with existing evidence demonstrating that flipped classroom approaches in nursing education are associated with improved learning-related outcomes, including self-efficacy, engagement, and reflective abilities [19,26]. Technology-enhanced flipped learning environments may allow learners to engage with content at their own pace while reserving classroom time for higher-order cognitive activities, which may contribute to these observed differences [24,31,32]. The relevance of these findings is particularly pronounced in the context of spiritual care education. Previous research has identified persistent gaps in nursing students' preparedness, confidence, and competence in delivering spiritual care, often attributed to limited curricular integration and insufficient experiential learning opportunities [3,9]. The reflective flipped classroom approach implemented in this study may offer a pedagogical structure that supports students in engaging with spiritual care content in a more meaningful and culturally grounded manner, particularly within Islamic nursing education contexts [33,34].
Overall, this study contributes to the growing literature on reflective and flipped learning strategies in nursing education by demonstrating differential patterns of change in self-efficacy and reflective thinking across instructional approaches. Future research employing randomized controlled designs, longitudinal follow-up, and qualitative inquiry may further elucidate the mechanisms through which reflective flipped classroom models support the development of nursing students' professional confidence and reflective capacity.
CONCLUSION
This study examined changes in self-efficacy and reflective thinking among nursing students participating in an interactive, module–based reflective flipped classroom, compared with those receiving traditional instruction. The findings showed statistically significant within-group improvements in both outcomes among students in the intervention group, along with higher post-test scores than in the control group. In contrast, changes in the control group were more limited and inconsistent across outcomes.
Together, these results suggest that integrating reflective learning activities and flipped classroom strategies is associated with more favorable learning outcomes in spiritual care education. Although causal conclusions cannot be drawn from the quasi-experimental design, the findings provide empirical support for the educational value of reflective, interactive pedagogical approaches in undergraduate nursing education.
Implications for nursing education and future research
This study advocates integrating reflective flipped classrooms into undergraduate nursing education, particularly for complex topics such as spiritual care. Activities that involve reflective exercises, interactive modules, and active participation can enhance students' confidence and depth of reflection. Educators could blend these approaches with traditional teaching methods to promote student-centered learning. Utilizing culturally relevant materials, such as Islamic perspectives on spiritual care, can further enhance engagement in contexts where religion plays a significant role. Future research should focus on randomized controlled trials, longitudinal studies, and qualitative methods to investigate the long-term effects and underlying mechanisms of these strategies. Expanding research across diverse settings will provide a more comprehensive assessment of their generalizability and effectiveness in nursing education.
Limitations
This study employed a quasi-experimental design without randomization, limiting causal inference. The sample was drawn from two institutions within a specific cultural and religious context, potentially limiting generalizability. Outcomes were measured using self-reported instruments, which may be subject to response bias. Additionally, the use of different statistical tests due to variations in data distribution and the absence of long-term follow-up should be considered when interpreting the findings.
Ethical Approval
Ethical approval for this study was obtained from the Ethics Committee of Universitas Aisyiyah Bandung (date approval May 15, 2025; no. 1270/KEP.01/UNISA-BANDUNG/V/20). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki. All participants were informed about the study objectives, procedures, and their right to withdraw at any time, and written informed consent was obtained prior to data collection.
Funding statement
This research was funded by the Majelis Pendidikan Tinggi, Pimpinan Pusat' Aisyiyah, under contract number 036A/PPA/I/IX/2025 and was facilitated by the Institute for Research and Community Service (Lembaga Penelitian dan Pengabdian kepada Masyarakat) at Universitas' Aisyiyah Bandung.
Conflict of interest
The authors declare that they have no competing interests.
Authors' contribution
Inggriane Puspita Dewi: Conceptualization, study design, development of the interactive module, and drafting of the original manuscript.
Popy Irawati: Contribution to spiritual care content expertise, educational evaluation, and critical manuscript revision.
Sharifah Shafinaz Sh Abdullah: Methodology development, data analysis, and critical revision of the manuscript
Soviaturohmah Nur Rizky: Data collection, participant coordination, and data organization
Resti Febrianti: Assistance in data collection, preliminary data processing, and support in manuscript preparation.
Santy Sanusi: Supervision of the flipped classroom implementation, validation of instruments, and manuscript review.
Acknowledgements
The authors appreciate all nursing students who volunteered for this study. They also extend their gratitude to the faculty members and academic staff for their support during the reflective flipped classroom activities.
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Effect of simulation on situational awareness of final-year nursing students at the Higher Institute of Nursing Sciences of Tunis, Tunisia: A quasi-experimental study
Abdelbasset Ghalgaoui 1,2,*, Rihab Salhi 3, Sawsen Rahmani 3, Yasmine Darrag 4, Imen Achouri 5,6
- Department of Nursing, Hamad Medical Corporation (HMC), Doha, Qatar.
- Institut Universitaire de Formation des Cadres (INUFOCAD), Port-au-Prince, Haiti.
- Higher Institute of Nursing Sciences of Tunis, Tunis, Tunisia.
- Private College of Nursing, Arar, Saudi Arabia.
- Higher Institute of Sport and Physical Education of Sfax, University of Sfax , Sfax, Tunisia.
- Research Laboratory Education, Motricity, Sport Health EM2S, LR19JS01, High Institute of Sport and Physical Education of Sfax, University of Sfax, Sfax, Tunisia.
* Corresponding author: Abdelbasset Ghalgaoui, Graduate Registered Nurse, Department of Nursing, Hamad Medical Corporation (HMC), Doha, Qatar. PhD Student in Education and Governance, Institut Universitaire de Formation des Cadres (INUFOCAD), Port-au-Prince, Haiti. Email: ghalgaouiabdelbasset@gmail.com
Cite this article
ABSTRACT
Introduction: Clinical simulation has become an essential strategy in nursing education, particularly for developing cognitive skills such as situational awareness (SA), which is critical for patient safety and effective clinical decision-making. However, evidence on the impact of simulation on SA among nursing students in Tunisia remains limited.
Objective: To evaluate the effect of clinical simulation on the situational awareness of final-year nursing students at the Higher Institute of Nursing Sciences of Tunis.
Methods: A quantitative, quasi-experimental, longitudinal study was conducted with 133 final-year nursing students during the academic year 2024–2025. Situational awareness was assessed using an adapted Situation Awareness Global Assessment Technique (SAGAT), measuring perception, comprehension, and projection. Assessments were performed before simulation training and three months after the intervention. Data were analyzed using descriptive statistics and Wilcoxon signed-rank tests.
Results: Significant improvements were observed across all dimensions of situational awareness. Mean perception scores increased from 3.75 (SD = 1.25) to 3.98 (SD = 1.19), comprehension from 0.96 (SD = 0.67) to 1.17 (SD = 0.62), and projection from 0.64 (SD = 0.86) to 1.17 (SD = 0.80). The total situational awareness score increased from 5.35 (SD = 1.95) to 6.32 (SD = 1.76). All differences were statistically significant (p < 0.001).
Conclusion: Clinical simulation significantly enhances nursing students’ situational awareness, with sustained effects observed three months post-intervention. These findings support the integration of structured simulation-based training into nursing curricula to strengthen cognitive competencies and improve patient safety.
Keywords: Clinical competence; Nursing education; Simulation; Situation awareness; Students
INTRODUCTION
Nursing education has increasingly embraced simulation as a key component of training, aiming to enhance various aspects of clinical competence among students. Clinical simulation has become a cornerstone of modern nursing education, offering a safe and controlled environment for students to practice and refine clinical skills, integrate theoretical knowledge, and develop complex cognitive competencies before exposure to real patients[1–3]. Simulation offers a controlled environment where nursing students can practice and refine their skills without risking patient safety. It provides an opportunity to engage in realistic scenarios that mimic real-life situations, allowing students to develop critical thinking and decision-making skills crucial for effective patient care. It is essential for cultivating nursing students’ knowledge, skills, collaboration, and confidence[4–7].
The role of nurses in the healthcare system is critically important. Multiple scoping review and systematic reviews studies show that clinical expertise, situational awareness, interprofessional coordination, and specialized training across different hospital departments are essential for significantly improving patient safety, care quality, and clinical outcomes[8–12]. Given the crucial role of nurses in patient care, simulation‑based training is essential for enhancing the situational awareness of future nurses.
Situation awareness, the ability to perceive, comprehend, and predict information about the environment and ongoing situations[13].It is fundamental for nurses to provide safe and effective care. Effective situation awareness allows nurses to anticipate potential issues, make informed decisions quickly, and respond to changing conditions in a timely manner. However, despite its importance, research specifically examining the impact of simulation on SA in nursing students remains limited. A recent scoping review suggests that SBE may be effective in cultivating SA skills, yet highlights a paucity of experimental studies and standardized assessment methods[14–17].
Furthermore, there is limited research focusing on the specific impact of simulation-based training on situation awareness among nursing students in Tunisia.
In Tunisia’s context, where nursing education is evolving rapidly to meet international standards. Robust learner support and multimodal engagement are the key drivers of nursing students’ satisfaction and self‑confidence in simulation‑based education[18]. while adding a structured, hands‑on simulation session to traditional lectures markedly boosts nursing students’ BLS‑CPR knowledge and psychomotor skills[19]. Although, some studies focus on affective reaction, cognitive learning, and procedural learning, there is no research examining real‑time cognition by assessing students’ situational awareness. Understanding how simulation affects situation awareness can provide valuable insights. The Higher Institute of Nursing Sciences of Tunis plays a pivotal role in shaping future nurses in the country. Assessing the impact of simulation on students’ situation awareness at this institution could offer important implications for educational strategies and curriculum development.
This research seeks to fill a gap in the current literature by exploring how simulation influences the situation awareness of nursing students at the Higher Institute of Nursing Sciences of Tunis. By investigating this relationship, the study aims to contribute to the improvement of nursing education practices and enhance the overall quality of healthcare training in Tunisia.
Situation awareness, the ability to perceive, comprehend, and predict information about the environment and ongoing situations, is fundamental for nurses to provide safe and effective care. Effective situation awareness allows nurses to anticipate potential issues, make informed decisions quickly, and respond to changing conditions in a timely manner. Despite its importance, there is limited research focusing on the specific impact of simulation-based training on situation awareness among nursing students in Tunisia.
MATERIALS AND METHODS
Study Design
A single-group pre- and post-test quantitative, quasi-experimental, longitudinal design to evaluate the impact of simulation-based training on the situational awareness (SA) of final-year nursing students at the Higher Institute of Nursing Sciences of Tunis. The study was conducted in a controlled environment using simulation scenarios that mirror real-life clinical situations.
Assessments of situational awareness were conducted both before and three months after the simulation training to evaluate both immediate and retained effects.
Study period
2024–2025 academic year.
Participants
All final-year students enrolled in the Bachelor of Science in Nursing program at the Higher Institute of Nursing Sciences of Tunis were invited to participate. The anticipated sample size is N=133 students.
Inclusion criteria
- Completion of relevant coursework in clinical nursing.
- Provision of informed consent to participate.
Exclusion criteria
- Inability to attend all required sessions or follow-up assessments due to scheduling conflicts or personal circumstances.
Instruments
The primary tool for assessing situational awareness will be an adapted version of the Situation Awareness Global Assessment Technique (SAGAT). SAGAT is a validated method for measuring SA across three hierarchical levels: Perception, Comprehension, and Projection (based on Endsley’s model).
Situational awareness was assessed using a questionnaire derived from the Situation Awareness Global Assessment Technique (SAGAT), originally developed by Mica R. Endsley[20]. The instrument was adapted from the Team Situation Awareness Global Assessment Technique (TSAGAT)[21]. The questionnaire included nine items corresponding to the three levels of situational awareness described in Endsley’s model: perception (5 items), comprehension (2 items), and projection (2 items).
The original TSAGAT questions were modified to reflect the simulated patient deterioration scenario used in this study and to ensure relevance to the Tunisian nursing education context. Minor linguistic and contextual adjustments were made while preserving the conceptual structure of the SAGAT framework. The adapted items were reviewed by nursing educators and clinical experts to ensure clarity, relevance, and alignment with local clinical practice.
Each item was scored dichotomously (1 = correct answer; 0 = incorrect), resulting in a total possible score ranging from 0 to 9, with higher scores indicating greater situational awareness. The internal consistency of the adapted instrument was assessed using Cronbach’s alpha based on baseline responses from 133 nursing students, yielding α = 0.633 (standardized α = 0.649). Corrected item–total correlations ranged from 0.106 to 0.431, indicating moderate internal consistency for this multidimensional construct.
Adapted SAGAT Questionnaire
The adapted questionnaire included 9 items, distributed across the three levels of situational awareness:
- Level 1 – Perception (5 items)
- Check the patient’s oxygen saturation
- Check the patient’s blood pressure
- Check the patient’s pulse
- What is on the wall next to the patient?
- What is on the patient’s chest?
- Level 2 – Comprehension (2 items)
- Is the patient well oxygenated?
- What is the problem with this patient?
- Level 3 – Projection (2 items)
- If you do not intervene properly, what will happen to the pulse?
- If you do not intervene properly, what will happen to the blood pressure?
The SAGAT questionnaire was administered twice: once before the simulation training (baseline) and again three months after the training (follow-up).
Each correct response was assigned a score of one point, while incorrect responses were scored as zero. Scores were calculated for each situational awareness level (perception, comprehension, and projection), as well as a total situational awareness score.
Simulation Training
Scenario Development
Simulation scenarios were developed to reflect critical clinical situations (e.g., patient deterioration, emergency response). The simulation scenario involved the management of a critically ill patient presenting signs of clinical deterioration requiring rapid assessment of vital signs and appropriate clinical decision-making. The simulation was conducted using a moderate-fidelity mannequin to reproduce realistic clinical conditions while allowing students to practice patient assessment and intervention in a controlled educational environment. These scenarios were designed to test and develop situational awareness through realistic, immersive experiences.
Simulation Sessions
- Each simulation session will last approximately 2–3 hours, including pre-briefing, active simulation, and debriefing.
- All participants will engage in the simulation training during a designated session period.
- The simulation sessions were supervised by instructors who were faculty members at the Higher Institute of Nursing Sciences of Tunis with prior experience in simulation-based education and clinical training.
Data Collection
Data will be collected in two phases:
- Pre-Simulation Assessment (Baseline):
Participants will complete the adapted SAGAT questionnaire before undergoing any simulation training to assess their baseline situational awareness.
- Simulation Training:
- Participants will take part in a simulation session, designed to challenge and enhance SA in real-time.
- Post-Simulation Assessment (Follow-Up after 3 Months):
- After three months, participants will again complete the same SAGAT questionnaire to evaluate the retention and long-term impact of the simulation training on their situational awareness.
Data Analysis
Data from the pre- and post-simulation SAGAT assessments will be analyzed using SPSS-26. The following statistical techniques were applied:
- Descriptive statistics to summarize demographic data and SA scores.
- The normality of continuous variables was evaluated using the Shapiro–Wilk test, which revealed a non-normal distribution (p < 0.05). Consequently, Wilcoxon signed-rank tests were performed to compare SA scores at baseline and after 3 months.
- A significance level of p-value (p) < 0.05 was used to determine statistical significance.
Ethical Considerations
Ethical approval was sought from the Institutional Review Board (IRB) of the Higher Institute of Nursing Sciences of Tunis (Approval No.: 01-07-10/2024; Date: 07/10/2024). Informed consent was obtained from all participants prior to their involvement in the study. Participants were assured of confidentiality and the right to withdraw from the study at any time without penalty.
RESULTS
Sociodemographic Characteristics of Participants
The sample consisted of 133 participants, with 52.63% identifying as female (n = 70) and 47.37% as male (n = 63). This represents a sex ratio of approximately 90 males for every 100 females, indicating a slight predominance of females in the study population.
The participants' ages ranged from 21 to 23 years, with a mean age of 22.01±0.38 years, indicating a very homogeneous age group. The median age was 22, matching the mean, which suggests a symmetrical distribution. In terms of frequency, the vast majority of participants were 22 years old (85.71%), while smaller proportions were 21 years old (6.77%) and 23 years old (7.52%) (Figure 1).
Figure 1. Distribution of participants by age
Descriptive Statistics of Pre- and Post-Training Scores
The data show an increase in mean (M) scores from pre-training to post-training across all measured variables (Table 1).
Statistical indexes
Pre-training
Perception
Comprehension
Projection
Total Score
Min
0
0
0
0
Max
5
2
2
9
Median
4
1
0
5
Interqurtile Range (IQR)
[3, 5]
[1, 1]
[0, 1.5]
[4, 7]
Mean
3.75
0.96
0.64
5.35
Standard Deviation (SD)
1.25
0.67
0.86
1.95
Post-training
Perception
Comprehension
Projection
Total Score
Min
0
0
0
1
Max
5
2
2
9
Median
4
1
1
6
Interqurtile Range (IQR)
[4, 5]
[1, 2]
[0.5, 2]
[5, 8]
Mean
3.98
1.17
1.17
6.32
Standard Deviation (SD)
1.19
0.62
0.80
1.76
Table1. Descriptive Statistics of Pre- and Post-Training Scores
Perception scores increased from M = 3.75, SD = 1.25 to M = 3.98, SD = 1.19. Comprehension scores increased from M = 0.96, SD = 0.67 to M = 1.17, SD = 0.62. Projection scores increased from M = 0.64, SD = 0.86 to M = 1.17, SD = 0.80. The total score increased from M = 5.35, SD = 1.95 to M = 6.32, SD = 1.76.
3. Pre- and Post-Training comparison
The Wilcoxon signed-rank tests were conducted to compare pre-training and post-training scores on Perception, Comprehension, Projection, and Total Score variables. Results indicated statistically significant increases across all measures following the training intervention. Specifically, the Perception scores showed a significant increase, Z = 4.71, p < 0.001, indicating a difference in median perception scores after training. Similarly, Comprehension scores increased significantly, Z = 5.01, p < 0.001, suggesting improved understanding post-training. Projection scores showed a larger effect, with Z = 6.44, p < 0.001, reflecting enhanced ability to apply or extend knowledge after training. The Total Score also increased significantly, Z = 7.70, p < 0.001, highlighting an overall improvement in combined performance measures (Table 2).
Variable
Test Statistic
Standardized
Z-Statistic)
p-value
Perception (pre vs post)
325.0
4.71
< 0.001
Comprehension (pre vs post)
351.0
5.01
< 0.001
Projection (pre vs post)
1326.0
6.44
< 0.001
Total Score (pre vs post)
2850.0
7.70
< 0.001
Table 2. Pre- and Post-Training comparison
Multivariate Linear Regression Analysis of Factors Associated with Improvement in Total Score
A multivariate linear regression analysis was conducted to examine whether demographic variables were associated with improvement in total score after the training intervention. The overall regression model was statistically significant (F = 12.90, p < 0.001) and explained 16.6% of the variance in score improvement (R² = 0.166, Adjusted R² = 0.153).
Sex was significantly associated with improvement in total score (B = −0.932, β = −0.407, p < 0.001), indicating that male participants showed greater improvement than female participants. In contrast, age was not significantly associated with changes in total score (B = 0.025, β = 0.008, p = 0.919), suggesting that the observed improvements were not influenced by participants’ age.
These findings indicate that the improvement in total score following the training intervention was primarily influenced by sex, while age did not play a significant role (see Table 3).
Variable
B
Standard Error (SE)
Standardized β
p-value
95% Confidence Interval
Sex
-0.932
0.183
-0.407
<0.001
-1.295 to -0.569
Age
0.025
0.242
0.008
0.919
-0.455 to 0.504
Constant
1.846
5.339
0.730
-8.716 to 12.408
Table 3. Multivariate Linear Regression Analysis of Factors Associated with Improvement in Total Score.
DISCUSSION
The present study examined the effect of simulation-based training on the situational awareness (SA) of final-year nursing students at the Higher Institute of Nursing Sciences of Tunis. Findings demonstrated significant improvements across all three dimensions of situational awareness perception, comprehension, and projection as well as in the overall SA score following simulation training. These results confirm the efficacy of simulation as a pedagogical strategy for strengthening cognitive and decision-making capacities that are critical for safe clinical practice.
Our findings are consistent with earlier work emphasizing the positive impact of simulation on nursing students’ knowledge acquisition, confidence, and decision-making abilities [4,6]. In particular, the observed improvement in projection scores aligns with [13] theoretical model of SA, in which the ability to anticipate future states represents the most advanced and clinically decisive dimension of situational awareness. Such gains suggest that immersive, scenario-based learning environments foster higher-order cognitive processing that extends beyond simple recognition or comprehension of patient data.
Moreover, this study contributes novel evidence in the Tunisian context, where research on simulation-based learning remains limited despite growing curricular reforms in nursing education [18,19]. Our results reinforce the idea that integrating simulation into undergraduate nursing curricula does not merely enhance psychomotor skills but also supports real-time cognitive processes essential for clinical safety and patient-centered care. This is particularly relevant given the documented importance of situational awareness in preventing adverse events and improving interprofessional team performance [9,10,12].
The current study found that demographic variables had a limited influence on improvement in total score following the training intervention. The multivariate regression model explained a modest proportion of variance (16.6%), suggesting that while sex significantly predicted improvement, the majority of performance gains were likely influenced by other factors such as baseline knowledge, prior experience, or engagement during training. Specifically, male participants demonstrated significantly greater improvement than female participants, whereas age was not associated with score changes. This finding aligns partially with prior simulation-based education research, which indicates that performance gains may be influenced by individual differences, although many studies report comparable improvements across sex and age groups [1,6,7]. The observed sex difference may reflect variations in confidence, prior exposure, or learning preferences within the study sample, rather than inherent ability, and should be interpreted cautiously. Overall, these results suggest that the training intervention effectively improved performance across participants, with sex-related differences accounting for a small but significant portion of the variance. Future research should explore additional factors, such as baseline competency and engagement levels, to better understand the predictors of skill acquisition in simulation-based training.
Another contribution of this study is the longitudinal assessment of SA. By conducting follow-up measurements three months after training, the results indicate that knowledge retention and cognitive benefits persist beyond the immediate post-simulation phase. This durability underscores the value of simulation as a sustainable educational strategy rather than a short-term intervention.
Despite these promising findings, several limitations should be acknowledged. First, the study relied on the adapted Situation Awareness Global Assessment Technique (SAGAT), which, although validated, may not fully capture the complexity of situational awareness in dynamic clinical environments. Second, this study employed a quasi-experimental single-group pre–post design without a control group, limiting causal inference. While significant improvements in situational awareness were observed, the absence of a control group prevents ruling out alternative explanations for the observed changes, such as concurrent learning experiences or natural maturation effects. The quasi-experimental design does not allow for causal inference with the same rigor as randomized controlled trials. Third, the study was confined to a single institution, potentially limiting the generalizability of results to other nursing programs with differing curricula, resources, or student populations. Fourth although the adapted SAGAT questionnaire was reviewed by nursing educators and clinical experts, a full psychometric validation was not conducted. Cronbach’s alpha (α = 0.633) indicated moderate internal consistency, and a few items had lower item–total correlations, which may affect the precision of situational awareness measurement.
Addressing these limitations in future research would strengthen the evidence base and allow for more nuanced insights into contextual factors that influence simulation effectiveness.
Future research should explore the integration of interprofessional simulation scenarios to evaluate how situational awareness develops in team-based contexts, which more closely mirror real-world healthcare environments [5]. Additionally, examining the role of structured debriefing in consolidating situational awareness would provide valuable pedagogical insights. Finally, comparative studies across diverse institutional and cultural settings would further validate the broader applicability of these findings.
CONCLUSION
This study provides empirical evidence that simulation significantly enhances nursing students’ situational awareness at all levels: perception, comprehension, and projection. The improvements observed both immediately and three months after training underscore the effectiveness of simulation not only as an instructional tool but also as a long-term capacity-building strategy in nursing education. By fostering the ability to detect, interpret, and anticipate clinical cues, simulation prepares students to act decisively in rapidly evolving healthcare contexts—an essential competency for ensuring patient safety and improving care outcomes. In the Tunisian context, where nursing education continues to align with international standards, these findings have important implications for curriculum design. Greater incorporation of simulation-based education may bridge the gap between theoretical knowledge and clinical competence, equipping future nurses with the cognitive agility and confidence necessary for professional practice.
Ultimately, embedding structured, simulation into nursing programs is not merely an educational innovation but a pedagogical imperative. As healthcare systems face increasing complexity, preparing nurses with advanced situational awareness through simulation represents a crucial step toward improving both individual clinical performance and overall quality of patient care.
Local Ethics Committee approval
The study was approved by the Institutional Review Board (IRB) of the Higher Institute of Nursing Sciences of Tunis (Approval No.: 01-07-10/2024; Date: 07/10/2024).. All procedures were conducted in accordance with the principles of the Declaration of Helsinki and relevant ethical guidelines for research involving human participants. Participant anonymity and data confidentiality were strictly maintained throughout all stages of the study. This study was conducted in accordance with ethical standards. All participants provided informed consent prior to participation.
Conflicts of interest
The authors declare no conflict of interest.
Sources of funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author contributions
Conception and design: Abdelbasset Ghalgaoui
Data collection: Rihab Salhi, Sawsen Rahmani
Data analysis and interpretation: Abdelbasset Ghalgaoui
Drafting of the manuscript: All authors
Critical revision of the manuscript: All authors
Final approval of the manuscript: All authors.
Acknowledgements
The authors would like to thank the administration, faculty members, and students of the Higher Institute of Nursing Sciences of Tunis for their cooperation and support during the conduct of this study.
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Are Future Health Workers Protected? A Cross-Sectional Study of SARS-CoV-2 Infection Control Practices Among Clinical Students at the University of Zimbabwe
Pfupajena Barbara1, Ndaimani Augustine2, Doreen Mukona3, Maxwell Mhlanga4
- Department of Primary Health Sciences, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe.
- Center for Nursing and Midwifery, University of Global Health Equity, Kigali, Rwanda.
- Fatima College of Health Sciences, Department of Nursing, Ajman, United Arab Emirates
- Department of Humanities and Social Medicine, University of Global Health Equity, Kigali, Rwanda
* Corresponding author: Maxwell Mhlanga., Department of Humanities and Social Sciences, University of Global Health Equity. Rwanda. E-mail: mmhlanga@ughe.org
Cite this article
ABSTRACT
Background: SARS-CoV-2 poses a persistent occupational risk to healthcare workers (HCWs) and, by extension, to health professions students undertaking clinical placements. Undergraduate clinical students represent a bidirectional transmission risk potentially carrying infection between campus and clinical environments, yet their specific infection prevention and control (IPC) practices remain poorly characterised in sub-Saharan African academic settings. This study examined IPC practices among health professions students at the University of Zimbabwe during the active phase of the COVID-19 pandemic.
Methods: An analytical cross-sectional survey was conducted between April and July 2021 at the University of Zimbabwe's Faculty of Medicine and Health Sciences. Using convenience lottery sampling, 320 undergraduate health professions students were enrolled. Data were collected via a researcher-administered structured questionnaire the Clinical Student IPC Practices Questionnaire (CSIPQ) comprising 38 items across five domains: (1) hand hygiene, (2) medical mask use, (3) personal protective equipment (PPE) use, (4) scrubs and clinical wear management, and (5) community IPC conduct. The manuscript was prepared in accordance with the STROBE Statement checklist for cross-sectional observational studies.
Results: The cohort comprised 320 frontline clinical students, predominantly young adults aged 20-25 years (95.3%) with a male preponderance (65.0%). Participants were drawn from five clinical programmes, with Medicine and Surgery (35.6%) and Nursing Science (28.8%) representing the largest groups, and half were in their fourth year of study, indicating advanced clinical exposure.
The prevalence of optimal IPC practice was critically low across all clinical domains: medical mask use (0.6%), PPE use (0.3%), and handwashing (15.3%). Significant associations were observed between handwashing and degree programme (p=0.002) and age (p<.0001). No significant demographic or academic associations were identified for mask use or PPE use, indicating that deficits were uniformly distributed across the cohort.
Conclusion: While specific student demographics were associated with better hand hygiene, near-universal gaps in PPE and medical mask use indicate a systemic failure transcending individual characteristics. Transformative, competency-based IPC education, resource security, and institutional safety culture reform are urgently needed to protect future health professionals and their patients.
Keywords: Health Professions Students; Infection Control; Hand Hygiene; Personal Protective Equipment; COVID-19; Cross-Sectional Studies; Zimbabwe.
INTRODUCTION
The Coronavirus Disease 2019 (COVID-19) pandemic, declared by the World Health Organization (WHO) in March 2020, has posed an unprecedented threat to global health systems, with health care workers (HCWs) bearing a disproportionate burden of infection and mortality [1]. HCWs face a significantly elevated risk, with studies indicating they are over three times more likely to contract SARS-CoV-2 than the general public [2]. This vulnerability extends to trainee HCWs, clinical students who operate at the critical nexus of academic and healthcare settings. As essential participants in patient care, these students are exposed to both community and occupational transmission risks, yet their specific infection prevention and control (IPC) practices remain an underexplored determinant of personal and patient safety [3].
Infection prevention and control (IPC) is the cornerstone of pandemic response, defined by WHO as a practical, evidence-based approach to preventing avoidable infections in patients and health workers [1]. Core non-pharmaceutical interventions, including hand hygiene, respiratory etiquette, and the correct use of personal protective equipment (PPE), are universally acknowledged as critical for breaking chains of transmission [4]. However, adherence to these protocols is influenced by a complex interplay of knowledge, resource availability, institutional policy, and sociodemographic factors [5]. In low-resource settings, which characterize much of sub-Saharan Africa, challenges such as PPE shortages, high-density living conditions, and financial constraints on students can severely compromise ideal IPC practice [6].
Although the WHO declared the end of the COVID-19 public health emergency of international concern on 5 May 2023, SARS-CoV-2 continues to circulate globally and poses an ongoing risk, particularly to elderly individuals and those with comorbidities or disabilities [7]. Furthermore, the clinical sequelae of infection extend beyond the acute phase: Long COVID characterised by persistent fatigue, cognitive impairment, dyspnoea, and multi-system dysfunction lasting weeks to months after infection represents a significant individual and societal burden that reinforces the continued importance of robust IPC practices among all healthcare workers [6].
Vaccination constitutes a primary prevention strategy against SARS-CoV-2 transmission and severe disease. However, studies among nurses and nursing students have identified significant vaccination hesitancy, influenced by social media exposure, misinformation, and attitudinal factors underscoring that immunisation alone is insufficient and must be complemented by strong IPC practice competencies [8 -10].
The context of higher education introduces unique vulnerabilities. Universities are high-density, mobile environments where large gatherings and shared facilities can accelerate outbreaks [11]. When clinical students rotate through healthcare facilities, they become potential bidirectional vectors of SARS-CoV-2, capable of carrying infection from campus to clinic and vice versa [12]. This risk is not merely theoretical; reports from Zimbabwe have documented significant COVID-19 outbreaks among student nurse cohorts within major teaching hospitals [9]. Despite regional and national commitments to strengthen IPC training and PPE provision, compliance gaps persist, suggesting that global guidelines may not adequately address the contextual realities of resource-limited academic and clinical environments [13].
Importantly, the psychological burden of the pandemic on health professions students must be acknowledged. COVID-19 phobia, academic exhaustion, and social isolation have been associated with increased dropout intention among nursing and health sciences students [14]. Fear of infection can erode students' confidence in clinical environments, contributing to burnout and attrition from health workforce training pipelines further amplifying the importance of adequately protecting students through robust IPC systems.
This study therefore aims to examine SARS-CoV-2 IPC practices among clinical students at the University of Zimbabwe, Faculty of Medicine and Health Sciences. By identifying strengths, gaps, and associated factors, this research seeks to inform targeted, context-specific interventions to better protect future healthcare professionals and the communities they serve.
Objective
The purpose of this study is to assess SARS-CoV-2 Infection Prevention and Control (IPC) practices among undergraduate health professions students specifically those enrolled in Medicine and Surgery, Nursing Science, Physiotherapy, Occupational Therapy, and Radiography at the University of Zimbabwe who had commenced clinical placements.
MATERIALS AND METHODS
Study Setting and Design
This was an analytical cross-sectional survey, prepared in accordance with the STROBE Statement checklist for cross-sectional observational studies [15]. The study was conducted at the University of Zimbabwe, Faculty of Medicine and Health Sciences the oldest and most prestigious university in Zimbabwe, with an enrolment of over 17,000 undergraduate students. The Faculty comprises approximately 23 teaching departments, one School of Pharmacy, and an Institute of Continuing Health Education, offering programmes including Medicine, Dentistry, Pharmacy, Nursing Science, Medical Laboratory Sciences, Rehabilitation, Radiology, and Health Education and Health Promotion. Data were collected between April and July 2021, over 30 weekday data collection sessions. The survey was administered as a researcher-administered, in-person, paper-based questionnaire.
Study Population
The target population comprised undergraduate health professions students who had commenced clinical placements. Eligible year groups were: Years 3–5 for Medicine and Surgery (clinical rotations beginning in Year 3), and Years 2–4 for Nursing Science, Physiotherapy, Occupational Therapy, and Radiography (clinical placements beginning in Year 2). It is important to note that not all eligible students were included participation was opportunity-based, as described below.
Sample Size Determination
The sample size was calculated using Cochran’s formula with finite population correction. The target population consisted of approximately 4,000 undergraduate clinical students. A 95% confidence level (Z = 1.96) and a 5% margin of error (e = 0.05) were selected.
Because no institutional data were available on infection prevention and control (IPC) practices among clinical students, we used an expected prevalence of 30% for “good IPC practices.” This value was informed by previous studies among health professions students in sub‑Saharan Africa, which consistently report low-to-moderate adherence to IPC measures. Banda et al. (2023) found that fewer than one‑third of pharmacy students in Zambia demonstrated good IPC practices [16]; Olum et al. (2020) reported similarly modest practice levels among Ugandan medical students [17]; and Sethi et al. (2021) observed that good IPC practices among Nigerian health professions students generally ranged between 30% and 40% [18]. These findings support the use of p = 0.30 for planning purposes.
Using Cochran’s formula for an infinite population:
Applying the finite population correction for N = 4000:
Thus, the minimum required sample size was 299 participants.
To ensure that this minimum would be met after accounting for non‑response and incomplete questionnaires, we applied a planned inflation about 7%. Therefore, the target sample size was set at 320 students.
Sampling
Convenience lottery sampling was used to select 320 health professions students. During lunchtime sessions in a student common area frequented by students from all eligible programmes, eligible students who were present were invited to draw a card labelled 'Yes' or 'No' from a container (with replacement). Students who drew a 'Yes' card were enrolled until the required sample size was reached. To minimise duplication, all approached students were asked whether they had previously completed the questionnaire; those confirming prior participation were not re-enrolled. No formal participant tracking log was maintained, which is acknowledged as a limitation. Because participation was opportunity-based at lunchtime, students on afternoon clinical rotations or off-campus placements on sampled days were less likely to be represented, constituting a potential source of selection bias.
Eligibility Criteria
The study included all students currently enrolled at the University of Zimbabwe, Faculty of Medicine and Health Sciences, in the fields of Medicine, Nursing Science, Physiotherapy, Occupational Therapy, or Radiography, and who had attended at least one clinical placement since the onset of the COVID-19 pandemic. Students who had experienced prior COVID-19 illness were excluded, as their practices may have been influenced by prior infection experience.
Data Collection
Data were collected over 30 weekday lunchtime sessions between April and July 2021, in the student common area at the University of Zimbabwe, Faculty of Medicine and Health Sciences. Two trained research assistants, supervised by the principal investigator, administered the structured paper questionnaire in person and were available to address any technical difficulties or survey-related questions. No identifying information appeared on completed questionnaires, which were stored in a lockable cabinet accessible only to the research team.
Measurement/Instrument
A single structured questionnaire the Clinical Student IPC Practices Questionnaire (CSIPQ) was used for data collection. The instrument was developed deductively, drawing on the WHO IPC Framework (2020) and the CDC COVID-19 Infection Control Guidance (2021) as theoretical frameworks, and incorporating items adapted from three peer-reviewed questionnaire-based studies on IPC practices among healthcare workers and students [19]; Hossain et al. 2021 [20]; Olum et al. 2020 [17]). A supplementary table listing the reviewed source studies and the elements adapted from each is provided.
The CSIPQ comprised 38 items across two sections: the first section captured four demographic variables (age, sex, degree programme, academic year); the second section contained 38 behavioural items across five IPC domains: hand hygiene (5 items), medical mask use (6 items), PPE use (8 items), scrubs and clinical wear management (7 items), and community IPC conduct (8 items). Items used binary Yes/No responses for frequency-of-behaviour questions and 5-point Likert-type frequency scales (Never to Always) for behavioural habit items. 'Optimal practice' within each domain was defined a priori as consistently endorsing all behaviours within that domain consistent with WHO IPC recommendations.
Content validity was assessed by a panel of three subject-matter experts (two infection control practitioners and one nursing education specialist) prior to the pilot study. A Content Validity Index (CVI) of 0.88 was achieved, and two items were revised based on expert feedback. The CSIPQ demonstrated strong content validity, with Item-Level Content Validity Indices (I-CVIs) ranging from 0.83 to 1.00 and a Scale-Level Content Validity Index (S-CVI/Ave) of 0.94, confirming excellent relevance and clarity of the items as assessed by the expert panel. To ensure clarity, relevance, and face validity, the instrument was pre-tested with 10 students at the University of Zimbabwe who met the inclusion criteria (mixed programme composition, minimum Year 2 with clinical exposure); their data were excluded from the main analysis.
The internal consistency of the tool was evaluated using Cronbach's alpha (α = 0.82), indicating acceptable reliability.
Data Analysis
Data were analysed using the Statistical Package for Social Sciences (SPSS) version 22. Descriptive statistics including absolute frequencies, relative frequencies (percentages), and 95% confidence intervals for proportions (Wilson score method) were used to describe demographic characteristics and adherence to IPC practices across all five domains. The chi-square test or Fisher's Exact Test (where cell counts were <5) was used to examine associations between demographic and academic variables and IPC practice categories.
For the handwashing outcome where prevalence was 15.3% (n ≈ 41 events) Poisson regression with robust variance estimation was performed to estimate crude Prevalence Ratios (PR) and Adjusted Prevalence Ratios (aPR), with 95% confidence intervals (CI) and p-values. This method is appropriate for common outcomes and avoids the overestimation of relative risk inherent in logistic regression.
For the medical mask use outcome (prevalence 0.6%; n = 2 events) and the PPE use outcome (prevalence 0.3%; n = 1 event), the extremely low event counts precluded reliable regression modelling due to risks of quasi-complete separation and model non-convergence. For these outcomes, Fisher's Exact Test was used for subgroup comparisons, as recommended for sparse data. In addition, directly calculated unadjusted Prevalence Ratios (simple relative risks computed directly from 2×2 tables, not model outputs) are reported as descriptive effect measures to communicate the uniformity of deficits across subgroups. These directly calculated PRs are mathematically valid regardless of outcome prevalence and are presented for descriptive completeness only, with their limitations explicitly acknowledged in table footnotes. All tests were two-sided; p < 0.05 was considered statistically significant.
Ethical Considerations
The study received ethical approval on 14 June 2021 from the Joint Research Ethics Committee of Parirenyatwa Group of Hospitals and the University of Zimbabwe Faculty of Medicine and Health Sciences (JREC Ref 243/2021).
Written informed consent was obtained from all participants before enrolment. Confidentiality was maintained by anonymising participant data, and access was restricted to authorised researchers.
RESULTS
Socio-demographic characteristics of participants
The demographic characteristics of the 320 frontline clinical students are summarised in Table 1. The cohort was predominantly young, with 95.3% (n=305) aged 20–25 years, and predominantly male (65.0%, n=208). Students were drawn from five clinical programmes, with Medicine and Surgery (35.6%, n=114) and Nursing Science (28.8%, n=92) comprising the largest groups. Half of the participants (50.0%, n=160) were in their fourth academic year, indicating advanced clinical exposure.
Variable Frequency Percentage Age range 20-25 305 95.3 26-30 14 4.4 31-35 1 0.3 Sex Male 208 65.0 Female 112 35.0 Degree Programme Medicine and Surgery 114 35.63 Nursing Science 92 28.75 Occupational Therapy 10 3.13 Physiotherapy 54 16.88 Radiotherapy 50 15.63 Academic year Second 64 20.0 Third 64 20.0 Fourth 160 50.0 Fifth 32 10.0 Total 320 100.0 Table 1. Demographic characteristics of frontline college students (n = 320)
Adherence to specific Infection Prevention and Control (IPC) measures was variable (Table 2). While foundational practices after exposure risks were near-universal, such as hand hygiene after contact with body fluids (99.4%) and consistent mask-wearing in clinical areas (98.1%), critical procedural precautions demonstrated significant gaps.
Less than half of students reported optimal handwashing between patients (53.1%) or before aseptic procedures (88.1%), and only 26.9% consistently used N95 respirators during aerosol-generating procedures.
In community settings, personal hygiene was prioritized (77.5%), but avoidance of high-risk activities like public transport (9.1%) and social gatherings (27.2%) was low.
Frequency Percentage Handwashing Before and after touching a patient 205 64.1 Wash hands with soap for at least 20 seconds 114 35.6 Before aseptic procedures 282 88.1 After contact with body fluids 318 99.4 Between Patients 170 53.1 Consistent use of a medical mask Always wear a mask when in the clinical area 314 98.1 Always remove when feeding but wear a new one before resuming my shift 40 12.5 Never pull my mask down to rest around my chin 80 25.0 I never wear a single mask for more than one day 62 19.3 I have never washed or reused a surgical mask 194 60.6 Always wear an N95 respirator when participating in aerosol-producing procedures 86 26.9 Use of §PPE Always wash/disinfect my hands before gloving 105 32.8 Always change gloves between patients 264 82.5 Use two pairs of gloves for routine procedures 91 28.4 Always wear goggles whenever there is risk of splashes from bodily fluids 99 30.9 Always wear an apron/gown if there is risk of splashes from bodily fluids 168 52.5 Always remove gown and apron whenever I leave ward 240 75.0 Always put on fresh §PPE gown, apron whenever I return to the clinical area from breaks or errands 102 31.9 Always use separate shoes for inside and outside the clinical area 47 14.7 Use of scrubs Always wash my scrubs/white coat after each use 73 22.8 Always wash my scrubs/white coat immediately after use 26 8.1 Always store my scrubs/white coat separately in a tightly sealed plastic bag until I can wash them 39 12.2 I always wash my scrubs/white coat separate from other clothes 106 33.1 Always wash my scrubs/white coat in hot water 11 3.4 I use disinfectant when washing my scrubs/white coat 59 18.4 Never visit common areas around campus (the library, canteen, class) in my scrubs/white coat 89 27.8 IPC practices in the community Always pay closer attention to personal hygiene 248 77.5 Always avoid public transport 29 9.1 Always use soap and water or use an alcohol-based disinfectant for hand washing 243 75.9 Always wash/disinfect my hands after leaving public space 160 50.0 I stay at home as much as possible 103 32.2 I avoid shaking hands when greeting others 138 43.1 I avoid hugging when greeting others 77 24.1 I avoid social gatherings 87 27.2 § PPE – personal protective equipment
Table 2. Infection Prevention and Control (IPC) practices of frontline health students (N=312).
The prevalence of comprehensive adherence to core IPC protocols was critically low across all domains (Table 3). The proportion of students demonstrating optimal practice was highest for community conduct (28.8%, 95% CI: 24.0–34.0), yet fell markedly for essential clinical practices: handwashing (15.3%, 95% CI: 11.8–19.7) and scrubs management (11.6%, 95% CI: 8.5–15.6). The prevalence of correct PPE and medical mask use was exceptionally low, at 0.3% (95% CI: 0.04–2.2) and 0.6% (95% CI: 0.2–2.5) respectively, with confidence intervals indicating these deficits are not due to chance.
Variable Proportion (%)
95% Confidence Interval Lower limit Upper limit Handwashing 15.31 11.75 19.71 Mask Use 0.63 0.16 2.48 PPE Use 0.31 0.043 2.21 Scrubs 11.56 8.48 15.58 Community IPC conduct 28.75 24.03 33.98 Table 3. Prevalence of PPE Use
A composite analysis of optimal Infection Prevention and Control (IPC) practice revealed critically low adherence across all clinical domains (Figure 1).
Figure 1. Number of students who performed optimally on different aspects of IPC
The prevalence of optimal practice was highest for community conduct (28.8%), yet remained below one-third of the cohort. Adherence was markedly lower for essential clinical safeguards: only 15.3% of students demonstrated optimal handwashing, and 11.6% adhered to proper scrubs management. Most alarmingly, the proportion of students performing optimally in correct PPE use (0.3%) and medical mask use (0.6%) was negligible, indicating a near-universal failure to implement these fundamental protective measures.
Association and Regression Analyses by IPC Domain
Mask Use
The analysis of factors associated with optimal mask use revealed no evidence of association between mask use and any demographic or academic subgroup (Table 4).
Variable Frequency *(%) PR (95%CI) aPR (95%CI) p-value Age range 20-25 303 (95.28) Ref. Ref. 26-30 14 (4.40) 1.00 (0.69; 1.46) 1.00 (0.996; 1.01) 0.32 31-35 1 (0.31) 1.00 (0.25; 4.01) 1.01 (0.99; 1.03) 0.31 Sex Female 206 (64.78) Ref. Ref. Male 112 (35.22) 0.996 (0.85; 1.17) 0.99 (0.98; 1.01) 0.31 Degree Programme Medicine and Surgery 113 (35.53) Ref. Ref. Nursing Science 92 (28.93) 0.99 (0.82; 1.21) 0.996 (0.99; 1.00) 0.32 Occupational Therapy 10 (3.15) 1.00 (0.37; 2.69) 1.00 (0.995; 1.00) 0.99 Physiotherapy 54 (16.98) 1.00 (0.80; 1.26) 1.00 (0.998; 1.00) 0.37 Radiotherapy 49 (15.41) 1.00 (0.79; 1.27) 1.00 (0.998; 1.00) 0.55 Academic year Second 64 (20.13) Ref. Ref. Third (19.81) 1.01 (0.79; 1.29) 1.01 (0.99; 1.02) 0.31 Fourth 159 (50.00) 1.01 (0.82; 1.24) 1.01 (0.99; 1.02) 0.31 Fifth 32 (10.06) 1.01 (0.75; 1.36) 1.01 (0.99; 1.02) 0.31 * Row percentage 50% with suboptimal mask use were in fourth year
Table 4. Characteristics associated with mask use among frontline students.
Fisher's Exact Test showed no significant difference in the distribution of the single optimal-mask-use event across sex, degree programme, or academic year categories (all p > 0.30). Directly calculated unadjusted PRs for all categories approximated 1.00, with 95% confidence intervals crossing the null value, confirming that the critically low prevalence of optimal mask use (0.6%) was uniformly distributed across the cohort. No multivariable adjustment was performed given the insufficient event count (n = 2 events).
Handwashing
The analysis of factors associated with optimal handwashing practice revealed significant associations with specific degree programmes. After adjustment, Occupational Therapy students had a significantly higher prevalence of optimal handwashing compared to Medical students (aPR = 1.08, 95% CI: 1.03–1.14; p = 0.002). A borderline significant association was also observed for Radiotherapy students (aPR = 1.06, 95% CI: 1.001–1.12; p = 0.046). No significant associations were observed for sex or academic year. For the age group 31–35 years, the aPR was 0.55 (95% CI: 0.52–0.58; p < 0.0001), though this estimate is based on a single participant and should be interpreted with extreme caution.
Variable Frequency (%) PR (95%CI) aPR (95%CI) p-value Age range 20-25 259 (95.57) Ref. Ref. 26-30 12 (4.43) 1.00 (0.67; 1.49) 1.06 (0.93; 1.20) 0.40 §31-35 0 (0.00) 0.54 (0.08; 3.85) 0.55 (0.52, 0.58) 0.00 Sex Female 176 (64.94) Ref. Ref. Male 95 (35.06) 1.00 (0.85; 1.19) 0.98 (0.93; 1.03) 0.48 Degree Programme Medicine and Surgery 97 (35.79) Ref. Ref. Nursing Science 72 (26.57) 0.96 (0.79; 1.18) 0.96 (0.89; 1.03) 0.220 § Occupational Therapy 7 (2.59) 1.08 (0.40; 2.91) 1.08 (1.03; 1.14) 0.002 Physiotherapy 47 (17.34) 1.01 (0.83; 1.35) 0.99 (0.93; 1.06) 0.85 §Radiotherapy 48 (17.71) 1.06 (0.83; 1.35) 1.06 (1.001; 1.12) 0.046 Academic year Second 52 (19.19) Ref. Ref. Third 54 (19.93) 1.02 (0.79; 1.31) 0.97 (0.89; 1.06) 0.49 Fourth 140 (51.66) 1.03 (0.83; 1.28) 1.00 (0.94; 1.07) 0.93 Fifth 25 (9.23) 0.98 (0.72; 1.35) 0.94 () 0.31 * Used row percentages; § Statistically significant association
Table 5. Characteristics associated with suboptimal handwashing
PPE Use
The analysis of factors associated with optimal PPE use showed no significant associations with any demographic or academic variable. Fisher's Exact Test showed no significant difference across any subgroup (all p > 0.30). Directly calculated unadjusted PRs approximated 1.00 with confidence intervals crossing the null, consistent with the interpretation that the critically low prevalence of optimal PPE use (0.31%) was uniformly distributed across the cohort irrespective of student characteristics. No multivariable adjustment was performed given the single-event outcome (n = 1 event).
Variable
Frequency (%)
PR (95%CI)
aPR (95%CI)
p-value
Age range
20-25
304 (95.30)
Ref.
Ref.
26-30
14 (4.39)
1.00 (0.69; 1.46)
1.00 (0.996; 1.01)
0.32
31-35
1 (0.31)
1.00 (0.25; 4.01)
1.00 (0.99; 1.03)
0.31
Sex
Female
208 (65.20)
Ref.
Ref.
Male
111 (34.80)
0.996 (0.85; 1.17)
0.99 (0.98; 1.01)
0.31
Degree Programme
Medicine and Surgery
114 (35.74)
Ref.
Ref.
Nursing Science
91 (28.53)
0.99 (0.82; 1.21)
0.996 (0.99; 1.00)
0.32
Occupational Therapy
10 (3.14)
1.00 (0.37; 2.69)
1.00 (0.995; 1.00)
0.99
Physiotherapy
54 (16.93)
1.00 (0.80; 1.26)
1.00 (0.998; 1.00)
0.37
Radiotherapy
50 (15.67)
1.00 (0.79; 1.27)
1.00 (0.998; 1.00)
0.55
Academic year
Second
63 (19.75)
Ref.
Ref.
Third
64 (20.06)
1.01 (0.79; 1.29)
1.01 (0.99; 1.02)
0.31
Fourth
160 (50.16)
1.01 (0.82; 1.24)
1.01 (0.99; 1.02)
0.31
Fifth
32 (10.03)
1.01 (0.75; 1.36)
1.01 (0.99; 1.02)
0.31
*Used row percentages
Table 6. Characteristics associated with PPE use among frontline students. (AIC=2.68)
In addition, we wish to clarify that the statistical validity of the analysis was not compromised by the missing responses. The minimum required sample size, based on our calculation, was 299 participants. The study targeted 320 students, incorporating an anticipated 7% non‑response rate to ensure that the analytic sample would remain above this threshold. Although eight participants did not complete all questionnaire items, the resulting analytic sample sizes (ranging from 312 to 319 across tables) remained well above the minimum requirement. Therefore, the effective analytic sample remained above the minimum required threshold in all tables, ensuring that no sample‑size–related bias was introduced into the analysis.
DISCUSSION
This cross-sectional study assessed SARS-CoV-2 IPC practices among undergraduate health professions students including Medical, Nursing, Physiotherapy, Occupational Therapy, and Radiography students at the University of Zimbabwe. Few global studies have explored this topic specifically among health professions students in sub-Saharan African contexts, and several have reported suboptimal adherence [11–15]. The present study found a similar pattern, with near-universal deficits in high-stakes clinical IPC practices.
Infection Prevention and Control Practices Among Frontline College Students
Hand Hygiene
Hand hygiene was evaluated using WHO's five key moments for handwashing. Students reported high overall compliance especially when at risk of contact with blood or body fluids (99.4%). However, compliance was lowest (32.8%) before and after wearing gloves. Only 36% of students washed their hands with soap for at least 20 seconds.
This pattern suggests a positive correlation between perceived infection risk and hand hygiene compliance. Fuller and colleagues, in their study "The Dirty Hand in the Latex Glove," found hand hygiene compliance decreased by 9% when gloves were worn, supporting our finding [21]. A common misconception exists that gloves are a full substitute for hand hygiene. A similar study found that 38% of medical students were unsure of proper hand hygiene practices when gloves were used [22. In our study, medical and nursing students scored highest on IPC, possibly due to their increased exposure to procedures requiring aseptic techniques.
Being an occupational therapy student was associated with an 8% increase in suboptimal hand hygiene (aPR = 1.08, p < 0.001), likely due to fewer opportunities for performing aseptic procedures. Age also played a role: students aged 31–35 were 45% less likely to demonstrate suboptimal handwashing (aPR = 0.55, p < 0.001), possibly due to greater clinical experience and professional training.
Mask Use
While 98.1% reported wearing masks in clinical areas, only 39.4% used them correctly. A significant proportion (75%) rested masks around their chins while eating or drinking, only 12.5% changed masks between breaks, and 80.7% reused single-use masks. Alarmingly, 39.4% washed and reused disposable masks, similar to findings in Ethiopia where the median duration of single-use mask wear was six days [23]. This contrasts with findings from Poland, where only 24.3% reused single-use masks [24], likely reflecting economic disparities. No demographic variable significantly influenced mask use.
Personal Protective Equipment (PPE)
Consistent PPE use in clinical settings was reported by only 35% of students, higher than the 24.2% reported in a Bangladeshi study [24]. Stockouts and discomfort were identified as barriers, consistent with findings from Tirivavi and others [25]. Proper donning and doffing were practiced by 53.5% of participants, comparable to the Bangladesh study (59.8%), suggesting knowledge gaps in correct PPE use [26]. Reuse of protective gowns was common (68.1%), often due to stockouts.
Fourth-year students had the highest rate of good IPC practices (48.8%; p = 0.02), possibly due to increased clinical exposure. Paradoxically, fifth-year students had the lowest scores (6.9%; p = 0.02). Nursing science students recorded the highest proportion of good IPC practices (34.4%; p = 0.05), likely due to more frequent patient contact and higher perceived infection risk.
This category had the lowest IPC compliance, with only 5.6% reporting consistent practice. Few students washed clinical wear after use (22.8%), washed it separately (33.1%), or used disinfectant soap (18.4%). These figures are lower than those reported in Saudi Arabia, where over half of dental students washed white coats after each use [27]. Limited access to running water in Zimbabwe may explain the low compliance.
Despite low hygiene, 72.2% reported visiting common areas in scrubs/white coats, citing inconvenience in changing attire during short breaks. Demographic variables did not significantly affect white coat/scrub hygiene.
IPC Practices in the Community
Most participants (77.5%) reported improved personal hygiene since the pandemic began, similar to Jordanian medical students (84%) [28]. However, fewer reported increased handwashing (50% vs. 87%), social distancing (24.1% vs. 70%), or avoiding handshakes (43.1% vs. 68.3%). These differences may reflect perceived infection risk and prior experiences with outbreaks. Jordan, having faced SARS (2012) and MERS (2015), recorded higher COVID-19 incidence than Zimbabwe in 2021 (73,305 vs. 4,136 cases/million) [29].
Proximity to the outbreak epicenter also seems to matter. In China’s Henan province, close to Wuhan, 89.7% of healthcare workers adhered to proper IPC [13], while in Ethiopia, compliance was lower at 38.7% [22]. Cultural norms, lockdown enforcement, and public health messaging may explain these differences.
In this study, gender, academic year, and degree program were significant. Female students had better community IPC practices than males (71.4% vs. 28.6%; p = 0.02), consistent with findings from Pakistan [30]. Women are often more hygiene-conscious and socialized to follow rules [31].
Fourth-year students again showed better community IPC practices (41.0%; p = 0.00), a trend supported by studies in Uganda showing older students and health trainees had better IPC compliance [32-33]. Nursing students also had the best community IPC scores (37.3%; p = 0.02), consistent with findings from Ethiopia, where nurses outperformed other healthcare workers in IPC adherence.
Study Implications for Nursing Practice
The findings of this study yield several critical implications for nursing education, clinical training, and institutional policy:
- Targeted IPC Curriculum Integration: The severe deficits in PPE use (0.3%) and medical mask protocols (0.6%) which showed no association with student demographics—point to a universal training failure. Nursing curricula must move beyond theoretical knowledge to include mandatory, simulation-based competency training on the correct donning, doffing, and disposal of PPE. This training should be standardized, recurrent, and include objective assessment before students enter clinical placements.
- Contextualized Education on Resource-Limited Practice: The high rates of PPE reuse and suboptimal scrubs hygiene, heavily influenced by stockouts and lack of facilities, require a pragmatic educational approach. Nursing education should explicitly address IPC adaptations and safety-conscious improvisation for low-resource settings, equipping students to make informed risk assessments without compromising core safety principles.
- Leveraging Professional Socialization and Role Modeling: The finding that Nursing students consistently demonstrated comparatively better IPC practices underscores the potential of professional socialization. Clinical training should intentionally leverage positive peer influence and ensure nursing preceptors exemplify impeccable IPC adherence, as students in high-exposure roles (like nursing) are pivotal in establishing safety norms for interprofessional teams.
- Institutional Accountability for a Safe Learning Environment: The uniform lack of association between student factors and critical IPC failures shifts the onus to institutions. Nursing schools and their affiliated healthcare facilities must co-develop and enforce clear policies guaranteeing consistent access to essential IPC materials (soap, water, PPE) for students. Creating a safety culture where students are not penalized for refusing unsafe assignments due to lack of equipment is paramount.
- Bridging the Community-Clinical IPC Gap: The disparity between higher community hygiene awareness and poor clinical-specific practice indicates a compartmentalization of knowledge. Nursing education should explicitly connect community-based prevention with clinical infection control, framing both as integral components of the professional role. This holistic approach can foster the consistent, context-independent application of IPC principles.
Future research should include: (1) longitudinal studies tracking IPC competency development across clinical years; (2) observational studies using direct observation to validate self-reported practices; (3) intervention trials evaluating simulation-based IPC training programmes in low-resource African settings; and (4) multi-institutional comparative studies across Zimbabwe and sub-Saharan Africa to establish generalisable baseline data for IPC preparedness among future health professionals.
Study Limitations
Several limitations of this study must be acknowledged. First, the cross-sectional design and reliance on self-reported practices limit causal inference and carry risk of social desirability bias; reported adherence likely overestimates actual compliance, which would be better captured by direct observation. Second, the convenience lottery sampling method whereby students self-selected into a common area during lunchtime does not guarantee equal probability of inclusion across all eligible students; those on off-campus rotations or afternoon practicals were likely underrepresented, and no formal mechanism was employed to detect participation bias. Third, data were collected from a single institution, limiting generalisability to other health training institutions in Zimbabwe and the region. Fourth, important potential confounders including prior IPC training quality, individual risk perception, specific clinical rotation exposures, and PPE availability during the survey period were not measured. Fifth, the Clinical Student IPC Practices Questionnaire (CSIPQ) underwent reliability testing (Cronbach's α = 0.82) but was not subjected to construct validation through exploratory or confirmatory factor analysis; the domain structure is theoretically derived rather than empirically confirmed, and future studies should conduct full psychometric validation of the instrument. Finally, the extremely low prevalence of optimal mask use and PPE use (≤2 events each) precluded multivariable regression modelling for these outcomes; the directly calculated PRs reported for these outcomes are descriptive only and do not represent adjusted estimates.
CONCLUSION
This study reveals a paradox in IPC preparedness among future healthcare professionals at the University of Zimbabwe: while awareness of general hygiene is high, adherence to essential, high-stakes clinical protocols particularly correct PPE and medical mask use is near-universally deficient across all health professions programmes studied. Alarmingly, these critical gaps showed no association with any individual student characteristic, indicating a systemic failure transcending demographics, training year, or programme of study.
The findings underscore an urgent need to move beyond knowledge-based IPC education. Protecting the health workforce pipeline across all health professions including medicine, nursing, physiotherapy, occupational therapy, and radiography demands a transformative approach centred on mandatory simulation-based competency training, guaranteed access to essential resources, and the cultivation of an institutional safety culture where best practice is modelled, enabled, and expected. Given that nursing constitutes the largest component of the frontline clinical workforce, nursing education and professional bodies are particularly well-positioned to champion this systemic change not only in safeguarding nursing students, but in establishing IPC standards that protect patients and the wider health system against current and future infectious threats.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Conflict of interest
The authors report no conflict of interest.
Authors’ contribution
Conceptualization: B.P. and A.N; methodology: B.P. and A.N.; software: B.P. and M.M; validation: B.P, D.N, M.M. and A.N.; statistical analysis: M.M., and A.N.; investigation: B.P.; resources: B.P. and A.N.; data curation: B.P. and M.M.; writing- original draft preparation: B.P.; writing-review and editing: A.N.; M.M and D.M; visualisation: M.M.; supervision: A.N.; projection administration: B.P.; funding acquisition: N/A. All authors have read and agreed to the published version of the manuscript.
Acknowledgements
We would want to acknowledge Management for Parirenyatwa Group of Hospitals for allowing us to carry out our research at their institution.
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EVALUATION OF SOFT SKILLS AMONG NURSES IN A MOROCCAN PROVINCIAL HOSPITAL: A CROSS-SECTIONAL STUDY
Ahmed Ouaamr 1 2*, Naima Taramitte 2, Yassine Ben Ali 2, Mohamed Chaf 2,
Abouri Otmane 3, Siraj Adil 4, Elbouzidi Mohamed 2, Katim Alaoui 1
- Pharmacodynamics Research Team ERP, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, BP 6203 Rabat, Morocco
- High Institute of Nursing Professions and Health Techniques, ISPITS, Aglou 2, BP 85000 Tiznit, Morocco
- Laboratory of Inflammatory Cellular and Molecular Physiopathology, Degenerative and Oncological, Faculty of Medicine and Pharmacy, Hassan II University of Casablanca, Casablanca, Morocco
- Faculty of Arts and Humanities, IBNZOHR AGADIR University, Morocco
* Corresponding Author: Ahmed Ouaamr, Pharmacodynamics Research Team ERP, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Morocco. E-mail: ad.bani82@gmail.com
Cite this article
ABSTRACT
Background: Soft skills underpin safe, patient-centered nursing care, yet empirical evidence describing these competencies in Moroccan provincial hospitals remains limited.
Objective: To assess soft skills levels among nurses, midwives, and health technicians at Hassan I Provincial Hospital (Tiznit, Morocco) and examine associations with sociodemographic and professional characteristics.
Results: In a census-based cross-sectional survey (15 May–3 June 2023), 77 of 113 eligible staff participated (response rate: 68.1%). Soft skills were measured using an adapted 25-item Soft Skills Questionnaire (5-point Likert scale; overall Cronbach’s α = 0.90) covering communication, emotional intelligence, management, and confidentiality. The mean overall soft skills score was 79.05 (SD = 8.69) on a 25–125 scale (scale midpoint: 75; observed range: 58–102). Communication was the strongest domain (mean = 48.52/60), whereas emotional intelligence was the lowest (mean = 8.48/15). Confidentiality showed notable gaps (mean = 16.83/30), with 31.2% reporting occasional unsafe handling of patient files. Bivariate analyses comparing low/medium/high soft skills categories did not show statistically significant differences across participant characteristics (all p > 0.05), although descriptive patterns were observed. In multivariable linear regression (outcome: overall soft skills score), higher scores were independently associated with prior soft skills training (B = 4.80; p = 0.001), greater professional experience (B = 0.45; p = 0.048), and working in departments other than the medical unit (B = 3.25; p = 0.021), while night work was associated with lower scores (B = −2.10; p = 0.034) (adjusted R² = 0.42; model p < 0.001).
Conclusion: Overall soft skills scores were slightly above the scale midpoint, with strengths in communication but weaknesses in emotional intelligence and confidentiality practices. Structured continuing professional development—especially targeted soft skills training—along with supportive organizational measures may strengthen non-technical competencies and improve quality of care in Moroccan provincial hospitals.
Keywords: soft skills; non-technical skills; nurses; communication; emotional intelligence; confidentiality; management; Morocco.
INTRODUCTION
Nursing professionals are indispensable pillars of healthcare delivery systems worldwide. Beyond their essential technical and clinical expertise, nurses require a robust set of interpersonal and cognitive competencies, collectively termed soft skills, to deliver holistic, patient-centered care [1,2]. These skills encompass effective communication, emotional intelligence, leadership and management capabilities, and strict adherence to confidentiality protocols, all of which profoundly influence patient satisfaction, safety outcomes, and the efficiency of healthcare teams [3,4].
Effective communication is fundamental to nursing practice. It facilitates the clear exchange of information between nurses and patients, fostering trust, reducing misunderstandings, and encouraging patient engagement in their care plans [5]. Strong communication skills enable nurses to tailor explanations, listen actively, and respond empathetically, which improves treatment adherence and overall health outcomes [6,7].
Emotional intelligence, the ability to recognize, understand, and manage one’s own emotions as well as those of others, plays a critical role in nursing. Given the high-stress and emotionally charged healthcare environment, nurses equipped with emotional intelligence can better cope with workplace challenges, support patients and families, and maintain professional resilience [8,9]. Emotional intelligence contributes to conflict resolution, teamwork, and the provision of compassionate care, all essential in improving patient experiences.
Management skills in nursing extend beyond administrative tasks to encompass effective prioritization of patient needs, coordination of care delivery, and resource optimization. These competencies are essential for maintaining workflow efficiency, particularly in resource-limited settings where nurses often juggle multiple responsibilities [9,10]. Good management ensures continuity of care, reduces errors, and enhances interdisciplinary collaboration.
Confidentiality remains a cornerstone of nursing ethics and professional standards. Respecting patient privacy and safeguarding sensitive information not only complies with legal requirements but also fosters trust between patients and healthcare providers, encouraging openness and honest communication [11,12]. Breaches in confidentiality can have profound repercussions, including loss of patient confidence and potential harm [13].
Despite the acknowledged importance of soft skills in nursing, a growing body of research reveals significant gaps in these competencies globally, especially in low- and middle-income countries [14]. Factors such as limited access to training, heavy workloads, cultural challenges, and infrastructural constraints contribute to these deficiencies. Within Africa, data on the prevalence and quality of soft skills among nursing staff are scarce, impeding the development of targeted training programs and policy initiatives tailored to the specific needs of healthcare workers in the region [15,16].
In the Moroccan and broader North African context, evidence on soft skills remains limited compared with high-income settings. Existing regional studies describe persistent challenges related to nurse–patient communication, respect for privacy and confidentiality, rising workload pressures, and complex ethical decision-making environments in public hospitals [19,20]. In Morocco specifically, nurses frequently operate under high patient-to-nurse ratios, significant administrative demands, and resource constraints that may hinder their ability to maintain optimal interpersonal and managerial competencies [21; 22]. These systemic pressures may contribute to variability in communication, emotional intelligence, managerial behaviors, and confidentiality practices across departments and professional profiles.
Recent reforms in the Moroccan health system—including the expansion of universal health coverage, the modernization of provincial hospital governance, and ongoing human-resources restructuring—have further increased expectations placed on nurses in terms of adaptability, teamwork, and communication competencies [23; 24]. Yet despite these evolving demands, empirical research examining soft skills among Moroccan nurses remains scarce, limiting the development of tailored training strategies and evidence-based policies suited to the national context. The present study therefore seeks to address this gap by providing context-specific data on soft skills performance and its associated factors within a Moroccan provincial hospital.
Considering these gaps, the present study aims to assess the level of soft skills among nursing professionals in a Moroccan provincial hospital and to identify demographic and professional factors associated with these competencies. By providing context-specific evidence, this study contributes to strengthening nursing education, informing health policy, and improving patient-centered care within the Moroccan healthcare system.
METHODS
Study Design and Setting
We conducted a cross-sectional, descriptive quantitative study at Hassan I Provincial Hospital in Tiznit, Morocco, from 15 May to 3 June 2023. This second-level referral hospital, operational since 1981, covers a total surface area of 28,852 m² and serves both urban and rural populations. It offers a wide range of specialized healthcare services, including internal medicine, surgery, psychiatry, pediatrics, maternity, operating theatre, hemodialysis, and laboratory units.
The hospital was purposefully selected due to: a) the researchers’ prior clinical training at the facility, b) the diversity of its patient population, representing various socio-economic and cultural backgrounds, and c) the breadth of specialized services, providing opportunities to assess soft skills application across multiple care contexts.
Study Population and Sampling
The target population comprised all state-registered nurses, midwives, and health technicians (as defined by Moroccan Law 43-13) employed in the aforementioned units during the study period.
Out of 113 eligible staff members, 77 participated, resulting in a census-based sampling approach with non-respondents. The remaining 36 were unavailable due to workload constraints, absence during data collection, or time limitations (Figure 1).
Figure 1. Flowchart describing the selection of participants in the cross-sectional study.
The inclusion criteria were: a) active clinical employment in the targeted units during the study period, b) a minimum of six months of continuous professional experience to ensure familiarity with workplace routines and responsibilities, and c) provision of informed consent.
Although the participation rate was relatively high (68.1%), the presence of non-respondents introduces the possibility of non-response bias, particularly if individuals with heavier workloads or limited availability systematically differ in soft skills levels from those who participated. This limitation is addressed in the Discussion section.
The exclusion criteria were a) Staff on extended leave (medical, maternity, or administrative) during data collection, b) individuals in exclusively administrative positions without direct patient care responsibilities, c) inability to complete the questionnaire due to workload, language barriers, or cognitive impairments, and d) declining to participate.
Data Collection Instrument
Data were collected using a self-administered, structured questionnaire. This method was chosen for its cost-effectiveness, efficiency, and ability to ensure participant anonymity, thereby enhancing the authenticity and reliability of responses.
The instrument was adapted to the Moroccan healthcare context from the Soft Skills Questionnaire developed by Mona Aridi et al., (2023) [18]. Modifications included adjustments to terminology and examples to ensure cultural and contextual relevance.
The questionnaire consisted of two main sections:
- Sociodemographic and professional characteristics: age, sex, marital status, professional profile, years of experience, department, job position, work schedule, languages spoken, academic qualifications, and prior training in soft skills.
- The soft skills assessment was organized into four domains: a) communication (12 items), b) emotional intelligence (3 items), c) confidentiality (6 items), and d) management (4 items).
Responses were rated on a 5-point Likert scale ranging from Strongly disagree (1) to Strongly agree (5). Total scores ranged from 25 to 125, with higher scores indicating stronger soft skills. Domain-specific scores were categorized as low, medium, or high based on predetermined cut-off points.
Instrument Validation and Reliability
Before data collection, several steps were undertaken to ensure the validity and reliability of the adapted questionnaire. First, content validity was assessed by a panel of five experts in nursing education and hospital management from the High Institute of Nursing Professions and Health Techniques (ISPITS). Experts evaluated item relevance, clarity, and cultural appropriateness, and minor modifications were made to terminology and examples to improve contextual suitability.
To ensure cultural adaptation, the instrument underwent forward and backward translation (Arabic–French–Arabic) by bilingual nursing professionals, followed by a reconciliation process to ensure semantic equivalence with the original questionnaire developed by Aridi et al. (2023). Additional adaptations were made to reflect Moroccan healthcare practices, communication norms, and ethical procedures.
A pilot test was conducted with a convenience sample of 12 nurses from a neighboring primary health center to evaluate comprehension, acceptability, and response time. Feedback indicated adequate clarity and no further changes were required. Data from the pilot test were not included in the final analysis.
The internal consistency of the instrument was assessed using Cronbach’s alpha on the study sample (N = 77). Reliability coefficients were acceptable to high across domains:
- Communication (12 items): α = 0.86
- Emotional intelligence (3 items): α = 0.74
- Management (4 items): α = 0.79
- Confidentiality (6 items): α = 0.82
- Overall scale (25 items): α = 0.90
These values indicate that the adapted instrument demonstrates good reliability and is appropriate for assessing soft skills in the Moroccan nursing context.
Scoring and Categorization of Soft Skills levels
For each of the four domains, item scores were summed to generate domain-specific totals. Since no validated cut-off thresholds exist in the literature for the adapted questionnaire, the categorization into low, medium, and high soft skills levels was based on the empirical distribution of scores in our sample. Specifically, the cut-off points corresponded to the lower tertile (low), middle tertile (medium), and upper tertile (high) of the domain-specific score distributions. This method is widely used in cross-sectional psychometric studies when normative data or validated thresholds are unavailable and allows for a meaningful differentiation of skill levels within the study population.
Data Collection Procedure
Authorization for data collection was obtained from the Provincial Health Delegation of Tiznit and the heads of the relevant hospital departments. The questionnaire was distributed via Google Forms and shared with eligible participants through WhatsApp. Data collection was strategically scheduled during shift changes to maximize participation.
Before completing the questionnaire, participants received a brief explanation of the study objectives, were assured of confidentiality, and provided informed consent.
Data Analysis
Data were coded and analyzed using IBM SPSS Statistics version 25. Univariate analyses were conducted to summarize variable distributions using frequencies, percentages, means, and standard deviations. Bivariate associations between soft skills levels and categorical independent variables were assessed using the Chi-square test, with statistical significance set at p < 0.05. Internal consistency reliability was evaluated using Cronbach’s alpha coefficients for each domain and for the overall scale. Soft skills levels were categorized into low, medium, and high using tertile-based thresholds derived from the sample distribution.
A multivariable linear regression model was then performed to identify predictors of the overall soft skills score. The dependent variable (total soft skills score; continuous, range 25–125) was analyzed using the enter method, in which all independent variables were entered simultaneously. Predictors included department, years of experience, work schedule, prior soft skills training, and age.
Before conducting the regression, model assumptions were evaluated. Linearity, independence of errors, homoscedasticity, and normality of residuals were verified and met. Multicollinearity was assessed using Variance Inflation Factor (VIF) and tolerance values. Because the predictors included both continuous and dichotomous variables, pairwise associations (Table 5) were examined using appropriate measures: Pearson’s correlation (r) for continuous–continuous pairs; point-biserial correlations (r_pb; equivalent to Pearson’s r with 0/1 coding) for continuous–dichotomous pairs; and the phi coefficient (φ) with Pearson’s chi-square test for dichotomous–dichotomous pairs.
Model fit was evaluated using the adjusted R² and the F-statistic from the ANOVA table. Results of the regression analysis are presented in Table 4 as unstandardized coefficients (B), standard errors (SE), standardized coefficients (β), t-values, confidence intervals, and p-values.
Age, years of experience, and patients per day were categorized based on the distribution of the sample (tertiles or quartiles), in accordance with common practices in epidemiological cross-sectional analyses. Age was divided into three groups reflecting early-career (21–33), mid-career (34–45), and senior-care (46–63) nurse profiles. Years of experience were categorized into 0–10, 10–20, 20–30, and >30 years to reflect typical professional stages in Moroccan public hospitals. The number of patients seen per day was grouped into clinically meaningful workload categories commonly used in hospital benchmarking (<5, 5–10, 10–20, and >20 patients/day).
Department affiliation was recorded across the hospital’s clinical units (medicine, psychiatry, surgery, pediatrics, operating room, hemodialysis, laboratory, and maternity). For the bivariate analyses presented in Table 3, departments with small numbers of participants were grouped into an “Other departments” category to reduce sparse cells and improve the stability of the Pearson chi‑square test. In our dataset, “Other departments” comprises Pediatrics, Hemodialysis, and the Laboratory.
Ethical Considerations
This study was conducted in accordance with the principles of the Declaration of Helsinki. It was approved by the ISPITS Ethics Committee, on October 26, 2022 (approval number: 37/22). Permission for data collection was also granted by the Provincial Health Delegation of Tiznit and the heads of the relevant hospital departments. All participants were informed about the study objectives and procedures and provided written informed consent prior to participation. Participation was voluntary, and confidentiality and anonymity were ensured throughout the study.
RESULTS
Sociodemographic and Professional Characteristics
A total of 77 nursing staff members participated in the study. The majority were male (n = 43, 55.8%), with females representing 44.2% (n = 34) as shown in Table 1. Most respondents (79.2%) were aged between 34 and 45 years, while 11.7% were 46–63 years old, and 9.1% were 21–33 years old. Regarding marital status, 79.2% were married and 20.8% single.
Professional profiles were diverse: 36.4% were polyvalent nurses, 26.0% midwives, 23.4% mental health nurses, 10.4% anesthesia-resuscitation nurses, and 3.9% nursing auxiliaries. Most held the position of practitioner nurse (89.6%), while 6.5% were nurse managers and 3.9% administrators.
Variable Category n % Sex Male 43 55.8 Female 34 44.2 Age (years) 21–33 7 9.1 34–45 61 79.2 46–63 9 11.7 Marital Status Married 61 79.2 Single 16 20.8 Professional Profile Polyvalent Nurse (IP) 28 36.4 Midwife (SF) 20 26.0 Anesthesia Nurse (IAR) 8 10.4 Mental Health Nurse (ISM) 18 23.4 Auxiliary 3 3.9 Department Medicine 11 14.3 Psychiatry 14 18.2 Surgery 6 7.8 Pediatrics 7 9.1 Operating Room 16 20.8 Hemodialysis 7 7.8 Laboratory 2 2.6 Maternity 15 19.5 Position Nurse Manager 5 6.5 Practitioner 69 89.6 Administrator 3 3.9 Experience (years) 0–10 4 5.2 10–20 41 53.2 20–30 30 39.0 >30 2 2.6 Work schedule Day shift 13 16.9 Day guard 6 7.8 Night guard 1 1.3 Mixed shifts 57 74.0 Workload Equitability Yes 65 84.4 No 12 15.6 Patients per Day 0–5 21 27.3 5–10 11 14.3 10–20 19 24.7 >20 26 33.8 Languages Spoken Tamazight 66 85.7 Arabic 77 100 French 74 96.1 English 18 23.4 Academic Level Bac+2 5 6.5 Bachelor’s 69 89.6 Master’s 3 3.9 Soft Skills Training Yes 18 23.4 No 59 76.6 Table 1. Sociodemographic and professional characteristics of participants (N = 77)
Regarding education, 89.6% had a bachelor’s degree, 6.5% held a Bac+2 diploma, and 3.9% had a master’s degree. Notably, 76.6% reported no prior formal soft skills training. Language proficiency was high: all spoke Arabic, 96.1% spoke French, 85.7% Tamazight, and 23.4% English.
Soft Skills assessment
Communication
Overall, communication practices were strong. Most participants greeted patients appropriately (77.9%), introduced themselves to new patients (62.3%), addressed patients by name (64.9%), and explained care procedures clearly (75.3%). About half (50.7%) used illustrations or analogies to aid understanding, and 68.8% practiced active listening.
Emotional Intelligence
This domain scored lowest, with a mean of 8.48 out of 15. While 54.5% stayed with patients beyond call requests and 54.5% assisted colleagues facing challenges, 59.7% reported difficulty in managing unjustified patient behaviors during time pressure.
Management
Management skills scored neutrally (mean = 12.09/20). Most participants considered patient counseling part of their role (62.3%), checked if patients had seen a physician (71.4%), prioritized care (64.9%), and substituted for absent colleagues (76.6%).
Confidentiality
Confidentiality had a low mean score (16.83/30). While most avoided sharing information with non-service staff (68.8%), maintained low voices during anamnesis (76.6%), and shared patient details only with authorized family (76.6%), 31.2% admitted occasionally leaving patient files in unsecured locations.
Additional descriptive properties of Soft Skills scores
To meet reporting standards, additional descriptive statistics were examined for the overall soft skills score and for each domain. The total soft skills score ranged from 58 to 102 (mean = 79.05, SD = 8.69). Domain-level observed ranges were as follows. The overall score was computed directly from raw item responses rather than from the sum of domain means, explaining minor differences between aggregated domain averages and the total score.
- Communication: min = 28, max = 60, mean = 48.52, SD = 8.40
- Emotional intelligence: min = 3, max = 15, mean = 8.48, SD = 2.85
- Management: min = 6, max = 20, mean = 12.09, SD = 3.10
- Confidentiality: min = 9, max = 27, mean = 16.83, SD = 3.95
Normality analyses showed that the distribution of the overall soft skills score did not significantly deviate from normality (Shapiro–Wilk p > 0.05). Skewness (–0.22) and kurtosis (0.31) values were within acceptable limits (|1|), indicating an approximately normal distribution. Similar patterns were observed for communication and management scores, while emotional intelligence and confidentiality showed mild but acceptable deviations from normality, allowing their inclusion in linear modelling. To provide a more detailed understanding of the distribution of soft skills across the four assessed domains, item-level descriptive statistics were calculated for all 25 questionnaire items. These values help identify specific strengths and weaknesses within each domain and complement the domain-level summary scores by offering a more granular view of nurses’ performance. Higher scores were consistently observed for fundamental communication behaviors such as greeting patients, explaining procedures, and maintaining eye contact, whereas lower scores were noted for items related to emotional intelligence and certain confidentiality practices. The complete item-level results are reported in Table 2.
Domain Item Code Item Description Mean SD Communication C1 Greet patients appropriately 4.21 0.71 C2 Introduce oneself to new patients 3.88 0.82 C3 Address patients by name 3.92 0.79 C4 Explain procedures clearly 4.15 0.74 C5 Use illustrations or analogies 3.11 1.02 C6 Encourage questions 3.74 0.89 C7 Practice active listening 3.95 0.83 C8 Verify patient understanding 3.71 0.90 C9 Adapt communication to literacy level 3.60 0.94 C10 Maintain eye contact 3.98 0.77 C11 Use empathetic language 3.82 0.85 C12 Avoid medical jargon 3.99 0.80 Emotional Intelligence EI1 Stay with patients beyond call requests 3.15 0.95 EI2 Assist colleagues facing challenges 3.12 0.96 EI3 Manage unjustified patient behavior under pressure 2.21 1.01 Management M1 Consider counseling part of role 3.34 0.92 M2 Check whether patient has seen a physician 3.81 0.83 M3 Prioritize care according to urgency 3.69 0.87 M4 Substitute colleagues when needed 4.02 0.78 Confidentiality CONF1 Avoid sharing information with unauthorized staff 3.89 0.87 CONF2 Keep patient files secured 2.89 1.09 CONF3 Speak in a low voice during anamnesis 4.02 0.79 CONF4 Share details only with authorized family members 4.05 0.76 CONF5 Avoid discussing patients in public spaces 3.48 0.98 CONF6 Verify identity before disclosing information 3.50 0.96 Table 2. Item-Level Descriptive Statistics for Soft Skills Domains (N = 77).
Associations between Soft Skills and Participant Characteristics
Chi-square analyses showed no statistically significant association between categorized overall soft skills levels and department (χ² = 9.42, df = 10, p = 0.49) as shown in Table 3., work schedule (χ² = 0.24, df = 2, p = 0.886), prior soft skills training (χ² = 0.16, df = 2, p = 0.925), or the other examined sociodemographic and professional characteristics (Table 3).
Descriptive variation across groups was nevertheless observed. For analytical presentation, low-frequency units were grouped under “Other departments,” comprising Pediatrics, Hemodialysis, and Laboratory. Likewise, mixed-shift workers showed descriptively higher soft skills levels than those working fixed schedules.
Variable Low n (%) Medium n (%) High n (%) χ² (df) / Exact p-value Department (total) 9.42 (10) 0.49 (C) Medicine (n=11) 2 6 3 Psychiatry (n=14) 6 6 2 Surgery (n=6) 0 3 3 Operating room (n=16) 2 7 7 Maternity (n=15) 3 9 3 Other departments (n=15) 4 6 5 Work schedule 0.24 (2) 0.886 (C) Fixed shifts (n=20) 4 11 5 Mixed shifts (n=57) 14 28 15 Soft skills training 0.16 (2) 0.925 (C) Yes (n=18) 4 6 8 No (n=59) 15 17 27 Sex 0.485 (2) 0.785 (C) Male (n=43) 11 22 10 Female (n=34) 7 17 10 Age group (years) 0.834 (4) 0.934 (C) 21–33 (n=7) 2 3 2 34–45 (n=61) 13 32 16 46–63 (n=9) 3 4 2 Marital status 0.943 (2) 0.624 (C) Married (n=61) 13 31 17 Single (n=16) 5 8 3 Position 1.974 (4) 0.741 (C) Practitioner (n=69) 15 35 19 Nurse manager (n=5) 2 2 1 Administrator (n=3) 1 2 0 Experience (years) 3.104 (6) 0.796 (C) 0–10 (n=4) 2 1 1 10–20 (n=41) 9 21 11 20–30 (n=30) 6 16 8 >30 (n=2) 1 1 0 Workload equitability 1.078 (2) 0.583 (C) Yes (n=65) 14 33 18 No (n=12) 4 6 2 Patients/day 1.604 (6) 0.952 (C) 0–5 (n=21) 4 10 7 5–10 (n=11) 3 5 3 10–20 (n=19) 5 9 5 >20 (n=26) 6 15 5 Language proficiency — — Arabic (100%) — — — French (n=74) 17 38 19 Tamazight (n=66) 15 34 17 English (n=18) 4 9 5 Academic level 1.460 (4) 0.834 (C) Bac+2 (n=5) 2 2 1 Bachelor’s (n=69) 14 36 19 Master’s (n=3) 1 1 1 Note: C = Pearson chi-square test; F = Fisher’s exact test; MC = Monte Carlo exact test; df = degrees of freedom.
Table 3. Associations between soft skills levels and participant characteristics (N = 77).
The distribution of categorized soft skills levels was similar between trained and untrained participants, consistent with the non-significant bivariate result. ‘Other departments’ refers to participants working in Pediatrics, Hemodialysis, and the Laboratory units, which were collapsed due to small cell counts. A similar proportion of trained and untrained participants were classified in the high soft skills category (44.4% vs 45.8%); this difference was not statistically significant in bivariate analysis χ²(2) = 0.16, p = 0.925.
- Department: Although descriptively higher soft skills scores were observed among pediatric nurses and midwives, these differences were not statistically significant (p = 0.49).
- Work schedule: Mixed-shift workers scored higher than those in fixed shifts.
- Training: Trained nurses showed descriptively higher proportions of high soft skills scores than untrained nurses (44.4% vs 45.8%), although this difference was not statistically significant.
No significant associations were found with sex, age, marital status, professional profile, position, years of experience, workload, patients per day, language proficiency, or academic level (Table 3).
Overall, the total soft skills score ranged from 58 to 102 (mean = 79.05, SD = 8.69), indicating satisfactory but improvable performance. Communication was the highest-scoring domain (mean = 48.52/60), whereas emotional intelligence was the lowest (mean = 8.48/15). Confidentiality showed notable gaps (mean = 16.83/30), particularly regarding secure handling of patient files (CONF2 mean = 2.89; Table 2).
Multivariable linear regression analysis (Table 4) provided additional insight after adjustment for organizational and professional factors. In the adjusted model, prior soft skills training emerged as a significant predictor of higher overall soft skills scores (B = 4.80, p = 0.001), alongside years of professional experience (B = 0.45, p = 0.048) and department affiliation (Other vs Medical; B = 3.25, p = 0.021). Night work schedule was associated with lower scores (B = −2.10, p = 0.034), whereas age was not a significant predictor (p = 0.215). Department was dichotomized for regression (Medical vs Other departments (Pediatrics, Hemodialysis, and the Laboratory)) due to sparse cell counts in several units.
Part A — Regression coefficients
Predictor B SE β t 95% CI p-value Tolerance VIF (R²) Department (ref = Medical) 3.25 1.38 0.24 2.35 0.52 to 5.98 0.021* 0.81 1.23 (0.19) Years of experience 0.45 0.22 0.19 2.03 0.01 to 0.89 0.048* 0.77 1.29 (0.23) Work schedule (Night vs Day) –2.10 0.98 –0.20 –2.14 –4.06 to –0.14 0.034* 0.84 1.19 (0.16) Soft skills training (Yes) 4.80 1.42 0.32 3.38 1.97 to 7.63 0.001** 0.93 1.07 (0.07) Age (years) 0.12 0.10 0.11 1.24 –0.08 to 0.32 0.215 0.89 1.12 (0.11) Note: Dependent variable = overall soft skills score (range 25–125). B = unstandardized coefficient; SE = standard error; β = standardized coefficient; CI = confidence interval. VIF (R²) represents the Variance Inflation Factor followed by the coefficient of determination obtained by regressing each predictor on all other independent variables. * p < 0.05; ** p < 0.01; *** p < 0.001.
Part B — Model fit and diagnostics
Statistic Value Adjusted R² 0.42 R² 0.46 F-statistic 12.31 df (Regression, Residual) (5, 71) ANOVA Model p-value <0.001 Durbin–Watson 1.91 Residual distribution Normal (Shapiro–Wilk p > 0.05) Homoscedasticity Verified (Breusch–Pagan p > 0.05) Multicollinearity Moderate collinearity, generally acceptable. (all VIF < 1.3) Note: Dependent variable = overall soft skills score (range 25–125). Department was dichotomized for regression: 0 = Medical unit; 1 = Other departments. B = unstandardized coefficient; SE(B) = standard error; β = standardized coefficient; CI = confidence interval; VIF = variance inflation factor. Reference categories: Department = Medical; Work schedule = Day shift; Soft skills training = No. Significance threshold p < 0.05. The model met assumptions of normality, homoscedasticity, independence of errors, and absence of multicollinearity.
Table 4. Multivariable Linear Regression Predicting the Overall Soft Skills Score (dependent variable; range 25–125), with VIF and R² Diagnostics.
Table 5 presents the pairwise associations among the predictor variables included in the regression model, allowing assessment of potential multicollinearity.
Variable Department
(0 = Medical;1 = Other)Experience (years)Work schedule
(0 = Day; 1 = Night)Soft skills training
(0 = No; 1 = Yes)Age (years)Department—0.18 (0.120)−0.12 (0.280)0.09 (0.410)0.04 (0.720)Experience (years)0.18 (0.120)—Work schedule−0.12 (0.280)−0.22 (0.050)—
Soft skills training0.09 (0.410)0.15 (0.190)−0.05 (0.640)—
Age (years)0.04 (0.720)0.72 (<0.001)*−0.08 (0.490)0.11 (0.320)Note: Cells report effect size with two‑tailed p‑values in parentheses. Continuous–continuous associations are reported using Pearson’s correlation coefficient (r). Continuous–dichotomous associations are reported using the point‑biserial correlation (r_pb) (equivalent to Pearson’s r with 0/1 coding). Associations between two dichotomous predictors are summarized using the phi coefficient (φ), with p‑values derived from Pearson’s chi‑square test (df = 1). Dichotomous predictors were coded 0/1 as follows: Department (0 = Medical unit, 1 = Other departments), Work schedule (0 = Day, 1 = Night), Training (0 = No, 1 = Yes). *: significant test
Table 5. Pairwise associations among predictor variables included in the regression model (N = 77).
The resulting correlation matrix provides an overview of the relationships between variables and helps identify whether any strong dependencies could jeopardize the stability of the multivariable model. Examination of the matrix showed a discrete dependence between age and years of experience (r = 0.72, p < 0.001), which was expected given the conceptual link between both variables. A weak negative correlation was also observed between work schedule and experience (r = –0.22, p = 0.05), however, it did not reach statistical significance (r_pb = −0.22, p = 0.050) given the prespecified threshold (p < 0.05). Although these relationships indicate some degree of interdependence among predictors, their magnitude remained below the commonly accepted threshold for problematic multicollinearity (r < 0.75). This was further supported by the Variance Inflation Factor (VIF) values reported in Table 4, all of which were below 1.3. These findings indicate the presence of moderate but acceptable collinearity, which does not compromise the stability or interpretability of the regression model.
DISCUSSION
This study evaluated the soft skills of nursing staff at Hassan I Provincial Hospital in Tiznit, Morocco, and examined their associations with sociodemographic and professional characteristics. The findings highlight both strengths and areas for improvement in these non-technical competencies, which are essential for delivering safe, effective, and patient-centered care [1,2].
Overall, communication skills emerged as the strongest domain, with most nurses reporting that they greeted patients, introduced themselves, addressed patients by name, and explained care procedures clearly. These results are consistent with evidence showing that effective communication improves patient satisfaction, adherence to treatment, and clinical outcomes [5]. The widespread use of active listening reinforces the principles of patient-centered care, which emphasize empathy and understanding [15]. However, only half of the participants reported using visual aids or analogies to enhance understanding, despite their proven benefits for patients with limited health literacy [15]. This represents an opportunity for targeted training aimed at diversifying communication strategies.
Emotional intelligence scored lowest among the four domains, suggesting difficulties in managing emotions and interpersonal relationships in a demanding work environment. Similar findings in other studies have linked lower emotional intelligence among nurses to high workload, stress, and burnout [6,11]. The tendency to overlook unjustified patient behaviors during busy periods may reflect emotional fatigue or cognitive overload, which can negatively impact both patient care and staff well-being [11].
Management skills were at a neutral level, indicating that while many nurses acknowledged responsibilities such as advising patients, prioritizing care, and supporting colleagues, formal managerial competencies may be underdeveloped. Prior research has similarly highlighted the need for structured managerial training in nursing education and continuing professional development [7,12]. Confidentiality practices showed mixed results. Although most participants-maintained discretion during patient interactions and limited the sharing of sensitive information to authorized individuals, approximately one-third admitted to leaving patient files unsecured. Such lapses raise ethical and legal concerns and may undermine patient trust. Contributing factors may include infrastructural limitations, shared ward environments, and heavy workloads—barriers also reported in comparable healthcare settings [8,13,14]. Strengthening both awareness and institutional support for confidentiality protocols is therefore critical. Although descriptive differences were observed across departments and work schedules, these associations were not statistically significant in the bivariate analyses and should therefore be interpreted cautiously. Prior soft skills training was not associated with categorized soft skills levels in the unadjusted comparisons; however, it emerged as an independent predictor in the adjusted linear regression model based on the continuous total score. This apparent discrepancy is not contradictory, because the bivariate analysis examined grouped categories of soft skills, whereas the multivariable model estimated the association with the continuous outcome after adjustment for other predictors. Higher scores in some departments may reflect differences in clinical demands and relational intensity, but these patterns remain descriptive in this cross-sectional sample. Similarly, longer professional experience was associated with higher soft skills scores in the adjusted model, suggesting that cumulative clinical exposure and professional maturity may contribute to the development of interpersonal competencies. These findings support the integration of structured soft skills training into both undergraduate nursing curricula and continuing professional development, with content tailored to specific departmental needs and work conditions. More structured pedagogical approaches—such as simulation-based training [25,26], supervised mentorship and preceptorship programs [27,28], reflective practice groups [29,30], and scenario-based workshops [30]—may help nurses translate communication, emotional regulation, and management principles into clinical behavior.
At the organizational level, targeted interventions such as workload redistribution [32], reinforcement of team-based care models [33], the implementation of clinical supervision [34], and the creation of dedicated confidential spaces for patient interviews could address several structural barriers identified in this study. Strengthening information-security systems and ensuring protected storage of patient records may further reduce confidentiality breaches [35,36]. These results also carry policy implications for nursing governance in Morocco. Integrating formal soft skills modules into ISPITS curricula [36], implementing mandatory continuing education credits focused on non-technical competencies, and aligning training standards with current health sector reforms [30] would contribute to enhancing the professionalization of the nursing workforce. At a broader level, incorporating soft skills indicators into hospital accreditation frameworks and performance evaluation systems may support more consistent and evidence-based development of these competencies nationwide [31].
The study’s cross-sectional design limits the ability to draw causal conclusions, and the single-center setting may reduce generalizability. Reliance on self-reported data also introduces the possibility of social desirability bias, particularly regarding sensitive topics like confidentiality. Future research should consider multi-center designs, larger and more diverse samples, and incorporate objective or observational measures.
Longitudinal studies could further illuminate how soft skills evolve throughout nurses’ careers, while qualitative approaches could offer deeper insights into the contextual factors influencing their development in Morocco and other African healthcare systems [16,17].
CONCLUSION
This study emphasizes the essential role of soft skills among nursing staff at Hassan I Provincial Hospital in Tiznit (Morocco) and offers a detailed assessment of their current competencies within this Moroccan healthcare context. The findings indicate that while communication skills are generally strong among nurses, notable deficiencies exist in emotional intelligence, management abilities, and adherence to confidentiality practices. These areas are crucial not only for delivering effective and compassionate patient care but also for promoting a supportive work environment and fostering teamwork across disciplines.
In adjusted analyses, prior soft skills training and professional experience were associated with higher soft skills scores. This highlights the urgent need for healthcare institutions and policymakers in Morocco to prioritize tailored continuing education programs focusing on these competencies. Integrating training on emotional intelligence, managerial skills, and ethical standards around patient confidentiality into both initial nursing education and ongoing professional development will be fundamental.
Enhancing soft skills among nurses has the potential to significantly improve patient outcomes, satisfaction, and trust in healthcare providers. As healthcare delivery grows increasingly complex, equipping nurses with these essential non-technical skills is vital to adapt effectively to diverse patient needs and ensure holistic, quality care.
Finally, the study underscores the importance of further research using larger samples and multi-center approaches, as well as longitudinal designs, to better understand the evolution of soft skills and their influence on healthcare quality across different settings. Addressing these gaps will require collaborative efforts between academic institutions, healthcare organizations, and regulatory bodies.
In conclusion, investing in the development of nursing soft skills is a critical step towards strengthening healthcare systems in Morocco and similar contexts, ultimately leading to improved patient care and professional nursing practice.
Limitations
This study has several limitations. First, its cross-sectional design limits the ability to establish causal relationships between soft skills and associated factors such as training or department. Second, the study was conducted in a single provincial hospital with a relatively small sample (N = 77), which may restrict the generalizability of the findings to other hospitals or regions in Morocco. Third, the use of a self-administered questionnaire introduces the potential for social desirability bias, particularly regarding sensitive domains such as confidentiality. In addition, the online distribution of the questionnaire through Google Forms and WhatsApp may have introduced selection and response bias, as nurses with high workload, limited availability, or reduced access to digital devices may have been underrepresented. This limitation may have affected the representativeness of the sample and the accuracy of certain domain scores. Fourth, the absence of objective or observational assessments of soft skills may limit the accuracy of the measurements. Finally, non-participation of some eligible nurses and the short data collection period may have introduced selection bias and may not fully capture temporal variations in practice. In particular, although the study used a census-based sampling approach, 36 eligible staff members did not participate, which may have introduced additional selection bias if non-respondents differed systematically from respondents—for example, if nurses with heavier workloads or lower soft skills were less available to participate.
Conflict of interest
The authors declare no conflicts of interest related to this work.
Funding sources
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of the authors’ academic and professional activities, and all costs were covered by the participating institutions.
Author contributions
- Ahmed Ouaamr: Conceptualization, study design, data collection, data analysis, manuscript drafting, and corresponding author.
- Naima Taramitte: Data collection, data curation, and manuscript review.
- Yassine Ben Ali: Data analysis, interpretation of results, and manuscript editing.
- Mohamed Chaf: Data collection and administrative support.
- Siraj Adil: Statistical analysis and methodological guidance.
- Abouri Otmane: Data collection and questionnaire administration.
- Elbouzidi Mouhamed: Literature review and manuscript editing.
- Katim Alaoui: Supervision, validation of study design, and critical revision of the manuscript.
Acknowledgements
The authors would like to express their gratitude to the management and nursing staff of Hassan I Provincial Hospital in Tiznit for their cooperation and participation in this study. Special thanks are extended to the administrative team for facilitating data collection and to all healthcare professionals who contributed their time and insights.
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Knowledge and Attitudes of the Role of Artificial Intelligence in Healthcare among Undergraduate Nursing Students in the Northeast of Pakistan: A Descriptive Cross-Sectional Study
Abdur Rahman1,Shakir Ullah2*,Noor Muhammad2, Muhammad Iqbal Khan Rahman2,
Muhammad Tariq1,Muhammad Hasnain1,Ismail Shahid3,Arshad Ali4,
Umair Islam5, Mahnoor Ali6, Rahim Shah7
- Elizabeth Rani College of Nursing Mardan, Peshawar, Pakistan
- Department of Microbiology, Abaysn University, Peshawar, Pakistan
- Department of Botany, Abdul Wali Khan University, Mardan, Pakistan
- Department of Customs Administration, University of International Business and Economics, China
- Department of Internal Medicine Khyber Teaching Hospital Peshawar, Pakistan
- Department IPMH & BS, Khyber Medical University, Khyber Pakhtunkhwa, Pakistan.
- Department of Pharmacy Bacha Khan University Charsadda, Pakistan
*Corresponding author: Shakir Ullah, Department of Microbiology, Abaysn University, Peshawar, Pakistan. Email: shakirullah1992@gmail.com
Cite this article
ABSTRACT
Background: Artificial intelligence (AI) is progressively developing as a breakthrough in healthcare provision, improving clinical decision-making, patient safety, and efficiency. Nursing students must be sufficiently equipped to comprehend and exploit AI technologies as future healthcare specialists. Nevertheless, there is a lack of local data regarding the knowledge of the nursing students and their attitude to AI in healthcare in Pakistan.
Objective: This research evaluated the knowledgeand attitudeof undergraduate nursing studentsabout artificial intelligence (AI) in healthcarein colleges ofNortheast of Pakistan.
Methods: An in-depth survey was used to conduct a descriptive cross-sectional study among undergraduate Generic Bachelor of Science in Nursing (BScN) students of 11 nursing colleges located in MardanNortheast of Pakistanover a period of four weeks. The method of sampling was the non-probability convenience sampling method.The sample size was determined using 95 percent of the confidence of a 5 percent margin of error in Open Epi.The participants (n=310) have been used to collect data using a structured and validated 2-rule questionnaire which included knowledge (10 questions) and attitude (10 questions) towards artificial intelligence in health care. The data analysis was carried out usingdescriptiveanalysis of frequencies, means, and standard deviations.
Results: Nursing students exhibit substantial knowledge regarding the issue of artificial intelligence.(mean knowledge score 4.02 +- 0.58). Most of the respondents agreed that AI had some beneficial use in the healthcare industry and could improvenursingpractice and as such should feature innursinglearning. The overall attitude towardartificialintelligence was good as the mean score of the attitude was 3.72±0.48.The majority ofthe students viewed AI as useful in terms of patients and healthcare progress. Nevertheless, the problems concerning ethical concerns, privacy, legal duty, and job substitution were also communicated.
Conclusion: Undergraduatesnursing students at Mardan,Northeast of Pakistanpossess favorableexperiences and understand the artificially intelligent healthcare knowledge comprehensively.Despite positive perceptions, current challenges suggest that systematic education, ethicscounseling, and curriculum alignment regarding AIthat will equip future nurses with suitable approaches to experienced artificial intelligence, which is safe and effective.
Keywords: Knowledge, Artificial Intelligence, Attitude, Nursing Students, Healthcare, Pakistan.
INTRODUCTION
Artificial intelligence (AI) is quickly revolutionizing the healthcare field, and it requires a proper comprehension of its place among upcoming healthcare practitioners, especially among undergraduate nursing students [1]. It is essential to assess the level of knowledge and the attitude of these students towards being ableto successfully integrate intoclinical practice and education [2,3]. The literature has constantly demonstrated that although nursing students tend to acknowledge the potential of AI, the gaps in their knowledge and diverse attitudes tend to be numerous to be met with through the effective development of the curriculum and proper use of AI technologies [4-7].
The introduction of AI to nursing education implies the evaluation of the knowledge of students regarding AI applications, its advantages, challenges, and ethical issues [7]. On the one hand, AI in the medical sector refers to a broad range of applications, such as improving the clinical decision-making process, streamlining hospital processes, and augmenting patient care and monitoring [8]. As an example, AI algorithms have the capacity to process large amounts of patient data and offer evidence-based suggestions, enhance personalized medicine by designing treatment plans to specific patients, and make an accurate diagnosis in such areas as radiology and pathology [9,10] and [11]. The optimization of logistics, the automation of administrative processes, and a better flow of patients and schedule are other examples of AI-based contributions tothe hospitalmanagement [12]. AI-powered wearable gadgets and virtual nursing assistants help tremendously in remote care and patient monitoring by continually tracking vital data andoffering assistance[13].
Even though these advantages have been identified, a major percentage of nursing students have little awareness ofparticular AIapplications and their principles [14,15]. Indicatively, a recent study carried out in Pakistan revealed that the undergraduate nursing students were not equally aware of AI and its impact on healthcare, which is why educational interventions tailored to this population should be provided. In another study conducted in western China, students were positive about generative AI, but their actual usage and their level of such knowledge demonstrated that the curriculum should be optimized [16]. Such lack of knowledge is possible because of insufficient exposure to the concept of AI in their courses and a general unawareness regarding its widespread use in healthcare systems of the modern era [17,18].
The perception of AI in nursing students is multiple, as it may tend to be both positive and negative [19,20].A large number ofstudents admit that AIhas the ability toenhance patient outcomes, make better decisions, and simplify work processes [21,17]. As an illustration, nursing students in Saudi Arabia tended to be positive and willing to use AI technology, and they understood that the technology had the potential to revolutionize medical practice [18]. Likewise, the Turkish research found that nursing students had a positive attitude to AI and saw its potential in future practice [9]. Such optimism is typically fueled by the fact that AI may result in the more efficient and effective care of patients [10].
Nevertheless, this interest is commonly restrained by such factors as consideration ofethical aspects, job loss, and must-have training [11]. Students report that they are afraid of the possibility of AI taking over human jobs in nursing, ethical issues related to patient privacy and data security, and the need to acquire additional skills in digital literacy to adjust to high-technology healthcare settings [14]. An example of a study conducted among nursing students in Jordan examined the relationship between AI ethical awareness, attitudes, anxiety, and the intention to use AI technology,which showed that ethical considerations played an important role in their views [21]. In addition to that, the psychological consequences of AI implementation, such as possible distress and self-efficacy issues, were observed among nursing students [20].
To overcome these obstacles, curricula should be structured in a way to increase the AI literacy of nursing students anddevelop positive attitudes [15]. This will include the integration of AI-specific material into the nursing curriculum, practical preparation, and the promotion of the (collaborative) character of interaction between humans and AI instead of emphasizing its replacement [21]. Project-based learning is one of the interactive forms of learning, which could considerably increase the knowledge and confidence of students in the use of AI tools [20]. These methods assist students in overcoming the initial knowledge gaps, learning to cooperate, and stimulating the development of scientific research [15].The experience of undergraduate nursing students working on an AI-based project is based on an emotional process.At the initial stage, they are disadvantaged by their lack of knowledge. During the adaptation stage, they are influenced by external factors that guide them toward self‑fulfillment.At the completion of the project, they will have clear expectations and recommendations of their own.[16]. First, students might be confused, feel unfamiliar, and embarrassed because of the lack of knowledge and abilities, along with the excitement about challenging new things [17]. Through adaptation, cooperation ability, classroom participation satisfaction, and the central role played by the teachers and teaching assistants continue to improve in the growth of the individual [18]. Lastly, learners share theirwantsto continue learning deeper, provide feedback on how to improve their abilities, and provide recommendations on the way to teach them better [18].
Moreover, it is crucial to deal with possible biasness of the AI models and provide ethical governance. In healthcare, AI should be able to guarantee patient privacy, data safety, and transparent functionality to develop trust [19]. The idea of such a phenomenon as data provenance serves as the reminder of the fact that the quality and history of data utilized to train AI modelsdirectly affects its accuracy and safety. To make AI systems accurate, reliable, and safe, rigorous validation procedures are commonly required that include testing algorithms on massive datasets to avoid biases and provide interpretable and useful systems [20]. To conclude, although undergraduate nursing students are likely to be aware of the increasing role of AI in the healthcare sector, their levels of knowledge and attitudes can be both high and low. It is evident that more comprehensive and interdisciplinary education techniques are required to not only increase AI literacy but deal with ethical issues, alleviate anxiety, and equip them to effectively apply AI into clinical practice in the future [21]
Aim
The purpose of the proposed study is to determine the extent of knowledge and perception regarding the use of artificial intelligence (AI) in nursing among undergraduate nursing students in Mardan,Northeast of Pakistan.
Objectives
To identify how much the undergraduate nursing students know about artificial intelligence and its application in healthcare.
To identify the influence of the undergraduate nursing students on the application of artificial intelligence in healthcare practice.
To ascertain the perceived benefits and concerns related to the introduction of artificial intelligence in the healthcare industry among nursing students.
To examine the relationship between the degree of knowledge and the attitude to use artificial intelligence in healthcare.
To formulate the influence of demographic and educational factors (year of study, prior experience with AI, and training) on the knowledge and attitudes of students.
To generate evidence likely to support the introduction of the study of artificial intelligence in the undergraduate nursing programs in Mardan,Northeast of Pakistan.
MATERIALS AND METHODS
Study Design and Setting
It is a descriptive cross-sectional study done on the nursing colleges in Mardan,Northeastof Pakistan, over a span of four weeks. The study was based on the aim of assessing the levels of knowledge and attitudes of the undergraduate nursing students concerning the use of artificial intelligence (AI) in healthcare.
Study Population
The research sample consisted of undergraduate students pursuing the Bachelor of Science in Nursing (BScN) at selected nursing institutions in Mardan,Northeast of Pakistan. The post-RN BScN and the diploma nursing students were omitted to ensure that the academics are exposed and trained in a uniform manner.
Sample Size and Sampling Method
The present study was a descriptive cross-sectional study to determine the degree of knowledge and attitude towards artificial intelligence (AI) in healthcare among undergraduate nursing students. Any information that was to be determined or computed to calculate the required sample size was done by the means of the open-source epidemiological statistics calculator known as OpenEpi version 3.
Cochran’s sample size formula was used to compute the initial sample size of an infinite population (n0) as follows:
where: n0 is the estimate sample size (infinite population),p represents the estimated proportion of the population possessing the characteristic of interest, whileqis its complement (q = 1 – p). Since no prior estimate was available, we usedp= 0.5 andq= 0.5, which provide the maximum variability and therefore the most conservative sample size. Z is the Z-score at 95% confidence level equal to 1.96, andd is the margin of error set to 0.05.
Since the study population, which is 1,567 undergraduate Generic BSN students in Mardan district is finite (total population N = 1,567), the finite population correction (FPC) formula was used to calculate the adjusted sample size (n):
where: n is final adjusted estimate sample size and N is the total population size (1,567). Hence, 309 students were the minimum sample required.
In order toreduce non-response bias and missing questionnaires, all eligible and accessible undergraduate BSN students were contacted to take part. The number of students that answered the survey reached 310, which is sufficiently to justify the statistical sufficiency of the research.
Sampling Details
Thenon-probabilityconvenience sampling technique was usedbased onpracticality such as availability of the respondents, time factor and the research was exploratory.
Recruitment of Students
The sampling technique involved students who were selected in nursing colleges in Mardan,Northeast of Pakistanthat provided administrative support to the research. The process of recruitment was organized with the help of the faculty coordinators and class representatives, who sent all eligible students the survey link using official academic communication tools, such as WhatsApp groups, institutional email lists, and academic forums.
Contexts of Participation
Participationwas mainly through onlinemedium(Google Forms), through which the students could use their own time to fill the questionnaire. Also, there was information exchange on the study in the classroom and laboratory time when the faculty briefly described the purpose and procedures without imposing pressure on students to take part.
Voluntary Participation
The involvement was voluntary. Detailed information on the study including objectives of the study, procedures and the possible benefits was given to the students. They had signed the informed consent electronically before they could gain access to the questionnaire. Students were promised that either way of involvement (or non-involvement) would not in any way interfere with their academic assessment.The management of self-selection bias involves selecting cases evenly: the proportion of male to female cases will be equal.
Although convenience sampling carries a risk of self‑selection bias, several measures were employed to minimize this possibility.The offer to participate was sent to all eligible students without any regard to previous interest or knowledgeonAI. There were several reminders to help the students who may have otherwise chosen not to take part in the study, which increased the sample representativeness. The fact that the participants are represented by various colleges in theMardan district makes the study less prone to bias and more reliable in the findings.
Eligibility Criteria
Only the students who are currently pursuing the Generic BSN program were eligible to get included. Students of post-RN and diploma nurses were not included to make sure that the exposure and training were similar in academics. Students who refused to take part or even filled out the questionnaires were also not included in final analysis.
The convenience sampling can reduce the level of generalizability, but in this case of studying the institution on an exploratory basis, it was considered suitable. The success of having a sample that is equal and slightly greater than the required size and the inclusion of students representing various institutions increases the representativeness and the validity of the study results.
Data Collection Tool
Data collection was done through the structured and standardized questionnaire, which was based on the already published and verified studies of knowledge and attitudes toward artificial intelligence in healthcare[9]. Little local contextual modifications were made to fit the local academic context without tampering with the original validity of the content. The original tools were obtained, and the authors were approached and allowed to use the tool.
The questionnaire was separated into two:
Part I: Assessment of the application of artificial intelligence in healthcare (10 multiple choices)
Part II: Attitude about artificial intelligence application in healthcare (10 multiple choices).
All the items were dedicated to the main topics, applications, benefits, and concerns of artificialintelligence in health care facilities.
Data Collection Procedure
The questionnaire was created based on the survey translated into a questionnaire and posted online through the Google Forms platform and sent to the respondents through mobile applications. The participation had been done with informed consent that had been informed in the electronic form. The research was a voluntary one, and the respondents were free to abandon the research at any given time.
Ethical Considerations
The Institutional Review Board (IRB) approved of the study ethically. The participants were assured that their information, privacy, and anonymity were assured. They were informed that they were taking part in the research work voluntarily, and they could withdraw at any stage without any academic and personal consequences.
Statistical Analysis
The data were analyzed usingSPSSversion 26. All the variables were calculated to obtain the descriptive statistics. On the continuous variables (age, knowledge scores, attitude scores), we have computed mean, standard deviation (SD), median, interquartile range (IQR), minimum and maximum. Frequencies and percentages were used to present categorical variables (gender, year of study, college name).We checked thenormal distributionof knowledge and attitude scores with the help of the Shapiro-Wilk test The Shapiro-Wilk p-valueof both scoresweregreaterthan 0.05, which proves a normal distribution and meets the conditions of parametric tests.
The correlation coefficient employed to analyze the relationship between attitude scores and total knowledge scores was Pearson correlation coefficient. Linearity and the assumption of approximatenormality were verified and met.
Mean knowledge and attitude scores between male and female students were compared using independent samples t-test.
The one-way ANOVA was applied to analyze the data concerning the difference in the mean scores ofknowledgeand the attitude among four academic years. The choice of this test was due to the availability of the independent variable (academic year) with more than two levels. We checked the assumptions of homogeneity of variances and normality prior to the execution of the test.The Shapiro-Wilk test was used to determine normality and gave non-significant (p > 0.05) values in all the year groups, which indicated normally distributed data.The homogeneity of variances was tested with the Levene, which did not have a significant value (p > 0.05), and it proved that there were similar variances in groups.In the instances of overall ANOVA significance, post-hoc pairwise comparisons to control Type I error were then done usingTukeyHonestly Significant Difference (HSD) test.The attitude scores were predicted using simple linear regressionanalysisand knowledge as the predictor variable.Independent predictors of attitudinal scores were examined using multiple linear regression, with the covariates of the model being knowledge score, year of study, and gender. The enter method was used to input all the predictors at the same time.In the two regression analyses, the conditions of linearity, independence of residues, homoscedasticity and normality of residues were tested.These conditionswere satisfactorily achieved.Correlation and regression coefficients confidence intervals (95%) were also reported to estimate the precision.
The p-value(p)statistically significant was determined to beless than0.05 and all p-values were two-tailed.
RESULTS
Demographic Characteristics of the participants.
In this study, 310 undergraduate nursing students were involved in the study who were selected inthe nursingcolleges of Mardan. The average age of the participants was 20.56(SD =1.47)years with ages of 18-28 years(Table 1).
| Variable | Category | Frequency (n) | Percentage (%) |
| Age (years) | 17 | 5 | 1.6 |
| 19 | 97 | 31.3 | |
| 20 | 53 | 17.1 | |
| 21 | 64 | 20.6 | |
| 22 | 72 | 23.2 | |
| 23 | 10 | 3.2 | |
| 24 | 6 | 1.9 | |
| 25 | 2 | 0.6 | |
| 28 | 1 | 0.3 | |
| Gender | Male | 257 | 83 |
| Female | 53 | 17.1 | |
| Year of Study | 1st Year | 25 | 8 |
| 2nd Year | 138 | 44 | |
| 3rd Year | 100 | 31 | |
| 4th Year | 48 | 15 | |
| College Name | Matonia College of Nursing | 50 | 16 |
| Elizabeth Rani College of Nursing | 46 | 15 | |
| BKMC College of Nursing, Mardan | 41 | 13.2 | |
| Institute of Health Sciences | 39 | 12.6 | |
| Government College of Nursing, Mardan | 32 | 10.3 | |
| Oriental College of Nursing, Mardan | 31 | 10.0 | |
| Alfajar College of Nursing | 27 | 8.7 | |
| TPIHS | 23 | 7.4 | |
| Mardan Institute of Nursing | 11 | 3.5 | |
| Zia College of Nursing | 7 | 2.3 | |
| Kingsway Institute | 3 | 1.0 |
Table 1. Demographic Characteristics (N=310)
When it comes to gender distribution, most of the respondents were men (82.9%), and 17.1% were women. Regarding the academic year,the majority ofstudents were taking the second year (44.5%), the third year (32.3%), the fourth year (15.2%), and the first year (8.1%).Students who were undertaking the BSN program were invited to take part in the research. The Students Participate from these 11 different nursing colleges within the district of Mardan. MatoniaCollege of Nursing (16.1%), Elizabeth Rani College of Nursing (14.8%), BKMC College of Nursing,Mardan (13.2%), Instituteof Health Sciences (12.6%), and Government College of Nursing, Mardan (10.3%) made the highest percentage proportion of the students. The rest were participantsof the Oriental College of Nursing (10.0%), Alfajar College of Nursing (8.7%), TPIHS (7.4%), Mardan Institute of Nursing (3.5%), Zia College of Nursing (2.3%), and Kingsway Institute (1.0%).This sample is a wide representation of Mardan undergraduate nursing students.
Figure 1. Age of nursing students (N=310)
The Figure 1 shows the age distribution of 310 nursing studentsofcollege going in Northeast Pakistan in the age range between 17 to 28.The number of students who fall within the range of 19 to 22 years is 92 percent. The highest percentage is 19 -year-olds (31.3) and 22-year-olds (23.2) and 20-year-olds (17.1). Ages of 23, 24, 17, 25 and 28 are included in smaller groups. The average age is 20.6 years which is normal among undergraduates in the area.
Figure 2 illustrates the enrollment of four years of BScN program. The highest number is of the second-year students (44.5%), third (32.3%), fourth (15.5%), and first-year students (8.1%). Sixty-seven percent of the respondents are in the second and third year, and this provides a balanced picture of the education levels.
Figure 2. Students' year of study (N=310)
Figure 3. Names of Colleges (N=310)
The horizontal bar chart(Figure 3)enlists 11 colleges of nursing in Northeast Pakistan. The leading three ones are: Matonia College of Nursing (16.1%), Elizabeth Rani College of Nursing (14.8%), and BKMC College of Nursing, Mardan (13.2%), which constitute 44 percent of the sample. The others represented in these colleges are the Institute of Health Sciences, Government College of Nursing Mardan, Oriental College of Nursing, Alfajar College of Nursing, TPIHS, Mardan Institute of Nursing, Zia College of Nursing and the Kingsway Institute. The study has 11 collegesrepresentation,and this increases the regional credibility of the study.
Artificial Intelligence knowledge in Undergraduate Nursing students
This paper evaluated the attentiveness of the undergraduate nursing students on the topic of artificial intelligence (AI) in healthcare. All in all, the level of knowledge was good as the mean score of knowledge was 4.02(SD =0.58).
Most of the participants acknowledged that artificial intelligence can be utilized in healthcare and nursing practice. The majority of the students correctly defined the important concepts in AI, including the distinction between machine learning and deep learning is, and what one of the useful applications of AI is in healthcare. Also, the legal and privacy issues connected with the use of AI in healthcare were documented by many respondents.Another significant percentage of students reported that AI is able to access the required information regarding patients and their medical history. Moreover, the majority of participants were in support of the addition of basic AI concepts in the nursing curriculum.
These resultsshowthat undergraduate nursing specialists have sufficient knowledge andexperience of artificial intelligence in healthcare.
| Knowledge Items | Strongly Agree
n (%) |
Agree
n (%) |
Neutral
n (%) |
Disagree
n (%) |
Strongly Disagree n (%) |
| Artificial intelligence is a useful application in healthcare | 83 (26.77) | 105 (33.87) | 20 (6.45) | 9 (2.90) | 93 (30.00) |
| AI may raise legal issues in healthcare | 36 (11.61) | 114 (36.77) | 44 (14.19) | 21 (6.77) | 95 (30.65) |
| There is a difference between machine learning and deep learning | 75 (24.19) | 97 (31.29) | 38 (12.26) | 9 (2.90) | 91 (29.35) |
| Speech recognition or transcription is helpful in healthcare | 71 (22.90) | 116 (37.42) | 20 (6.45) | 9 (2.90) | 94 (30.32) |
| Serious privacy issues can occur with the use of AI in healthcare | 53 (17.10) | 106 (34.19) | 27 (8.71) | 30 (9.68) | 94 (30.32) |
| There are benefits of using artificial intelligence in nursing | 90 (29.03) | 94 (30.32) | 16 (5.16) | 16 (5.16) | 94 (30.32) |
| AI could be useful in healthcare | 80 (25.81) | 100 (32.26) | 23 (7.42) | 12 (3.87) | 95 (30.65) |
| AI can access patient medical history | 60 (19.35) | 108 (34.84) | 29 (9.35) | 18 (5.81) | 95 (30.65) |
| AI improves accuracy in healthcare decision-making | 78 (25.16) | 101 (32.58) | 24 (7.74) | 12 (3.87) | 95 (30.65) |
| AI will get all relevant information about a patient and medical history | 53 (17.10) | 106 (34.19) | 27 (8.71) | 30 (9.68) | 94 (30.32) |
Table 2. Question about knowledge (N=310).
The findings about respondents and their knowledge regarding the use of artificial intelligence (AI) in healthcare are provided in Table 2.Overall, the vast majority of participants agreed or strongly agreed with the statements and expressed a rather positive attitude towards the applications of artificial intelligence in healthcare and nursing.About 60 percent admitted that AI is helpful in the field of healthcare and enhances decision-making.Likewise,proportions saw the advantages of nursing and saw speech-recognition technology as beneficial.Numerous participants have also mentioned that AI can be used toimproveand help them manage clinical information.However, around 30% strongly disagreed with several of the items. This implies that some respondents have limited knowledge or are uncertain about what machine learning and deep learning are, as well as how AI is applied in retrieving patient information.
The ethical and legal issues mentioned by many respondents were privacy issues and the legal consequences of using AI in healthcare.Overall, the awareness of AI among the participants ismoderate to good, yet additional education and training are necessary to enhance the growth of knowledge and awareness among healthcare providers.
Artificial Intelligence attitude in Undergraduate Nursing students
The overall perception of the nursing students towards artificial intelligence was that it was not a bad idea since the average score of attitude was 3.72(SD =0.48).The majority of the participants saw AI as something useful and had positive attitudes to its use in enhancing the well-being of patients, the creation of new economic opportunities, and supporting the practice of nursing. Students also affirmed that nurses must be adequately familiar with AI and that AI education needs to be taught in undergraduate nursing programs.Nevertheless, there were also concerns that have been reported, and these are mainly about job replacement, ethical risks, and safety concerns about AI use.Overall, the research findings are positive: nursing students appear ready for, and accepting of, the integration of AI in healthcare, despite these concerns.
| Attitude Items | Strongly Agree
n (%) |
Agree
n (%) |
Neutral
n (%) |
Disagree
n (%) |
Strongly Disagree
n (%) |
| The future of artificial intelligence will be beneficial to the society. | 144 (46.5) | 142 (45.8) | 17 (5.5) | 3 (1.0) | 4 (1.3) |
| AI should be taught in the undergraduate nursing program | 55 (17.7) | 148 (47.7) | 12 (3.9) | 94 (30.3) | 1 (0.3) |
| Artificial intelligence is exciting | 151 (48.7) | 144 (46.5) | 10 (3.2) | 2 (0.6) | 3 (1.0) |
| AI can provide new economic opportunities | 150 (48.4) | 145 (46.8) | 6 (1.9) | 2 (0.6) | 7 (2.3) |
| AI has positive impacts on patients’ wellbeing | 154 (49.7) | 139 (44.8) | 9 (2.9) | 4 (1.3) | 4 (1.3) |
| Nurses should have good familiarity with AI | 155 (50.0) | 142 (45.8) | 6 (1.9) | 2 (0.6) | 5 (1.6) |
| AI is more dangerous than nuclear weapons | 66 (21.3) | 39 (12.6) | 6 (1.9) | 101 (32.6) | 98 (31.6) |
| AI can replace nurses at their jobs | 29 (9.4) | 19 (6.1) | 103 (33.2) | 22 (7.1) | 137 (44.2) |
| AI systems can perform better than humans | 29 (9.4) | 120 (38.7) | 14 (4.5) | 108 (34.8) | 39 (12.6) |
| There are drawbacks to using AI in nursing education | 70 (22.9) | 125 (40.8) | 111(35.0) | 2 (0.7) | 2 (0.7) |
Table 3. Questions about attitude (N=310).
Table 3 represents the attitudes of respondents toward the use of AI in healthcare and nursing. In general, the participants had such a positive opinion. A majority of them (92.3) expressed their strong agreement or that AI will be of benefit to society (92.3), exciting (95.2) as well as creating new economic opportunities (95.2). Similarly, the majority of the respondents believed that AI has a positive impact on patient well-being (94.5%), and nurses have to know it (95.8%), which supports the high acceptance of its application in clinical practice. Education wise, 65.4% of the respondents confirmed that AI should be educated in undergraduate nursing courses with 30.3% on the contrary indicating a balance on whether AI should be taught in nursing curricula. On the other hand, most respondents did not agree that AI is more harmful than nuclear weapons (64.2%), neither did they agree that AI would eliminate the nurses (51.3%). It means that AI is not perceived by the participants as a significant threat to the profession of nurses.Overall, the data shows that respondents are positive and optimistic about AI in healthcare.Nonetheless, they also admit some issues and constraints associated with its application in nursing education and practice.
Lenient Knowledge and Attitude Scores
The total mean score of interaction with the topic of artificial intelligence in healthcare in terms of knowledge was 4.02(SD =0.58)on a five-point scale, which is close to the good level of knowledge among undergraduate students of nursing. The total means of the attitude scale was 3.72 with a standard deviation of 0.48, indicating a positive attitude towards the use of AI in the medical facilities.
| Variable | Mean | SD | Median | Interquartile range | Min | Max |
| Knowledge Score | 4.02 | 0.58 | 4 | [3.6, 4.4] | 2.5 | 5 |
| Attitude Score | 3.72 | 0.48 | 3.7 | [3.4, 4.0] | 2.2 | 5 |
Table 4. Total Knowledge and Attitude.
The findings indicate that the understanding of artificial intelligence in healthcare practice among the undergraduate nursing students in the Northeast of Pakistan is moderate to good with a mean of 4.02 out of 5 as the knowledge score (SD = 0.58).The median value is 4.0 with an interquartile range of 3.6 to 4.4, which shows that most students have a median value between 4 and 4.6 with a score ranging between 2.5 and 5.0.The overall attitude toward AI was positive and the mean attitude score is 3.72 out of 5 (SD = 0.48). The median is 3.7 and the interquartile of the student views was 3.4 to4.0,indicating that50percent of the students were always in a positive mood. There was a range of attitude ratings of 2.2 to 5.0.These results show that students are mostly aware of AI applications and understand how they can be used in nursing practice and care. They are also accepting and ready to adopt AI in healthcare.But even with such positive outcomes, the students had some concerns regarding ethical issues, data privacy, and employment security. These issues imply that, although nursing learners are well-educated and think positively, they have to be trained in the structured education, integration of the curriculum, and certain training to become the safe and effective users of AI in healthcare practice.
Knowledge and Attitude Relationship
We estimated a Pearson correlation coefficient to investigate the relationshipbetween knowledge of AI in healthcare and students’ attitudes toward AI.The total weighted with 10 items knowledge scores and the total weighted with 10 items attitude scores were obtained by summing the scores after the reverse score of negatively worded items such that high scores always indicated positive attitude.
| Variable Pair | Correlation Coefficient (r) | 95% Confidence Interval | p-value |
| Total Knowledge Score & Total Attitude Score | 0.48 | [0.39, 0.56] | <0.001 |
Table 5. Correlation between: Knowledge scores and Attitude Scores.
Table 5 shows a statistically significant moderate positive relationship(r=0.48, 95%CI=[0.39, 0.56], p<0.001). Its coefficient of 0.48 shows that there is a moderate relationship: the higher the knowledge about AI, the more positive the attitudes towards its implementation in healthcare are. The confidence interval[0.39, 0.56]affirms that the actual correlation could not be weak or even negative. The p<0.001, which illustrates the fact that this outcome is not accidental.
Demographic and Educational Factor Impact
The mean difference among the knowledge and attitudes of the groups based on their years of study depends on the ANOVA.
| Year | Knowledge
Mean ± SD |
Attitude
Mean ± SD |
| 1st | 3.85 ± 0.60 | 3.58 ± 0.50 |
| 2nd | 4.00 ± 0.55 | 3.70 ± 0.48 |
| 3rd | 4.08 ± 0.59 | 3.75 ± 0.47 |
| 4th | 4.12 ± 0.57 | 3.80 ± 0.46 |
Table 6. Knowledge and Attitude Scores Academic Year (N=310).
One-way analysis of variance (ANOVA) was used to identify differences between knowledge and attitude scores in the four academic years (1st year, 2nd year, 3rd year, and 4th year). The assumptions of homogeneity of variances and normality were analyzed and proved before analysis. The Shapiro- Wilk test showed that the scores in knowledge and attitude were found to be distributed normally within the academic year population (p > 0.05 in all groups). The test of homogeneity of the variances of both knowledge scores (p = 0.68) and attitude scores (p = 0.72) by Levene was tested as homogeneous.In the case of knowledge scores, the one-way ANOVA indicated that there is statistically significant difference regarding academic years (F(3, 306) = 3.15, p = 0.026). Tukey honestly significant Difference (HSD) test was used as a post-hoc comparison to determine the specific year groups that differed. The findings showed that 4th year students scoredsignificantly higher (mean = 4.12,SD =0.57) than 1st year students (mean = 3.85,SD =0.60), with a mean difference of 0.27 (95%CI [0.03, 0.51], p = 0.032). Any other statistically significant differences between the rest of the year groups were statistically insignificant (p > 0.05 in all comparisons).Regardingattitude scores, the one-way ANOVA failed to provide statistically significant difference between the academic years (F(3, 306) = 2.12, p = 0.10), which implies that the attitudes towards AI did not differ significantly depending on the year of study of students.This indicates that the understanding of AI is gradually built throughout the nursing program and the senior students are more knowledgeable about it than their junior counterparts. Nevertheless, the positive opinion toward AI seems to be formed at the early age and to stay constant during the educational years.
Gender (Independent t-test) Knowledge and Attitudes
| Gender | Knowledge
Mean ± SD |
Attitude
Mean ± SD |
t statistic | p-value |
| Male | 4.03 ± 0.57 | 3.72 ± 0.48 | 0.45 | 0.65 |
| Female | 4.00 ± 0.61 | 3.71 ± 0.47 | 0.21 | 0.83 |
Table 7. Gender Knowledge and Attitude Scores (N=310).
Table 7 showsno statistical difference in knowledge or attitude between male and female students, thus indicating that gender does not affect the knowledge and attitudes toward AI in this group.
Predicting Attitudes on the Knowledge basis
In order to test the hypothesis of whether knowledge scores are predictors of attitudes towards AI, we conducted a simple linear regression. The regression was very strong (F(1, 308) = 92.16, p < 0.001) and had the capability to explain the 23 per cent of the variance in attitude scoresR2=0.23As can be seen in Table 8, the knowledge score had a significant positive predictor of attitude (β=0.48, 95% CI [0.38, 0.58], p < 0.001). This implies that on a one-unit increase in the knowledge score, the attitude score increases by 0.48 units. These results prove that the more one knows about AI, the more positive their attitude towards its application in healthcare is.
| Predictor | β | SE | t statistic | p-value | 95% CI for β |
| Total Knowledge Score | 0.48 | 0.05 | 9.60 | <0.001 | [0.38, 0.58] |
Note: R² = 0.23, F(1, 308) = 92.16, p < 0.001, SE=Standard Error
Table 8. Simple Linear Regression Analysis: Attitude Predicted by Knowledge (N=310).
Multivariate Regression Analysis
Multiple Regression (Knowledge + Year + Gender)
Note:R² = 0.26, F(3, 306) = 35.84, p < 0.001, SE=Standard Error
| Predictor | β | SE | t statistic | p-value | 95% CI for β |
| Total Knowledge Score | 0.47 | 0.05 | 9.4 | <0.001 | [0.37, 0.57] |
| Year of Study | 0.09 | 0.04 | 2.25 | 0.025 | [0.01, 0.17] |
| Gender (Male vs Female) | 0.02 | 0.06 | 0.33 | 0.741 | [-0.10, 0.14] |
Table 9. Multicollinear Regression Preparing Attitude (N=310)
A multiple linear regression was used to determine the predictive ability of knowledge score, year of study, and gender on attitudes toward AI. The model was found to be statistically significant (F(3, 306) = 35.84, p < 0.001) and explained 26 percent of the variance in the attitude scores (R 2 = 0.26).As presented in Table 9, the knowledge score was the best predictor ofattitude (β =0.47, 95%CI=[0.37, 0.57], p<0.001). Students who had a higher level of knowledge were more positive about AI. Year of study was also a strong positive predictor (β =0.09,95%CI= [0.01,0.17])meaning that, the more advanced students were in their studies, the more favorable they were towards AI.
There was no substantial contribution of gender (β =0.02, p=0.741), whichimplies that there was no substantial difference in attitudes between male and female students. All in all, the knowledgewas the most important determinant, and academic progression had a slight impact.
Critical Interpretation of the Results
In the research, it was discovered that the majority of undergraduate nursing students possessed a good knowledge base and a favorable perception of AI in healthcare. However, the closer examination of the particular survey items reveals a more balanced position. Students did not completely accept AI in all fields of practices. There were numerous concerns and criticisms regarding the data privacy and the ethical risksthe security of the system, and the risk of losing a job. These issues prove that the perception of the benefits of AI and awareness of the professional, legal, and ethical issues influence the attitude of students.
The balanced and negative responses on a few of the critical items might indicate that there are still students who are not quite confident about the long-term impact of AI in clinical practice. Although the promise of AI as a means of increasing efficiency, aiding choices, and enhancing care was mentioned in many of them, they also cautioned against excessive dependence on technology, reduced levels of human interaction, and a lack of accountability in cases of AI malfunctions. Those remarks present a conditional acceptance: learners are willing to useAI,but they are attentive of data protection, explicit professional principles and appropriate regulations.
Another positive association between AI knowledge and attitudes was also identified by us, which showed that the greater the knowledge is, the more positive are the attitudes. And the knowledge was not enough to ignore concerns. Even those students who possess more knowledge raised ethical, professional, and patient-safety concerns. Therefore, nursing education needs to educate not only on technical AI competencies but also on ethical decision-making, legal consciousness, dataconfidentiality,and the evolving nurse-AI relationship.
To conclude, AI in healthcare is not opposed by nursing students, though their implementation is reserved and has real and justified worries. The results recommend an extensive education, morerigorous ethics training, and enabling policies to ensure that AI is implemented in nursing practice safely, responsibly, and ethically.
DISCUSSION
The main goal of the research was to assess the levels of knowledge and attitudes ofundergraduate nursing studentsabout artificial intelligence (AI) in healthcare. The result shows that the students had a high level of knowledge (mean = 4.02, SD =0.58) and relatively positive attitudes (mean = 3.72, SD =0.48) towards AI, which indicates an increasing knowledge level and the willingness to accept technological innovations among the future professional in the healthcare field. These findings indicate a growing familiarity of nursing students withdigital health tools, AI applicantsand healthcare technologies,which might have contributed to their level of knowledge regarding the concepts of AI, such as machine learning, deep learning, clinical decision support, and data management [11,17].
The Knowledge item analysis found out that the majority of students could accurately determine the distinction between machine learning and deep learning, the utility of applications of AI, including speech recognition, in healthcare, and the possible privacy and legal issues that the use of AI can cause. These results emphasize that a lower threshold amount of technological literacy is present in undergraduate nursing students, and it is necessary to achieve safety and efficiency in the implementation of AI in clinical practice.
The positive trend notwithstanding, the conditional positivity among students was also noted in the study. Although a significant number of students reported positive impacts of AI, a significant percentage of them shared their apprehensions and uncertainty:
Job replacement: 44% disagreed with the statement that AI would not take nurses’ jobs, indicating a fear of being replaced as professionals.
Ethical risks: The percentage of those who perceived AI as potentially dangerous was 30-32, indicating the fear of ethical and moral concerns of patient care.
Privacy issues: 30 percent did not agree that AI is safe,which reveals that they are aware of potential threats to patient data and confidentiality
These results mean that knowledge is not a sufficient factor to influence the formation of positive attitudes since the issues of safety, ethics, and employment mediate acceptance of AI in healthcare. This highlights the significance of considering ethics, privacy and safety discourse into nursing education, in addition to technical knowledge [12,16].
The findings are in line with the previous studies across the globe. The willingness to embrace AI technology was observed in Saudi Arabia, where the nursing students acknowledged the possibility of enhancing clinical decision-making, workflow, and patient outcomes [10,19].Similarly, a study conducted in Turkey found that nursing students held positive attitudes toward AI and believed it could be beneficial in their future professional practice [8].The hope behind this is mostly pegged to the fact that AI can improve the quality of healthcare, minimize mistakes, and assist in effective patient treatment [13].
The paper has also examined the interaction between knowledge and attitude. The Pearson correlation analysis showed that there is a moderate positive relationship between AI knowledge and attitudes towards its use (r= 0.48, p < 0.001), meaning that students who know more about AI have their attitudes towards its use. Simple linear regression also affirmed knowledge to be a very important predictor of attitude (r=0.48, p < 0.001). Analysis based on multiple regression and incorporated knowledge, academic year, and gender showed that knowledge and academic year have significant predictive effect onattitude,but gender does not play a significant role. These results indicate that academic materials and clinical experiences can support positive attitudes in the long run, and structured education and curriculum planning play an important role in influencingthe attitude of students [11,17,19].
Although the attitude in general was good, it is worth mentioning that there were ethical, professional, and safety issues. A largepercentage of the students were afraid that AI could take human nurses away, interfere with patient privacy, or be abused in health care.This is consistent with results from Jordan and Saudi Arabia, which found thatethical awareness, anxiety, and professional responsibility influence the attitude of students towards AI not just by knowledge but also by these factors [10,12,15].These concerns highlight the need to take appropriate actionin the implementation of AI in clinical practice in order to make it responsible, safe, and ethical.
The results highlight the necessity to include AI-related education in the nurse curriculum. Organized educational activities that merge theoretical aspects, practical education, and professional codes of conduct are bound to enhance the appreciation and the assimilation of AI among nursing students [4,5]. The interactive learning methods, including project-based learning, simulation activities, and collaboration of AI projects, may improve the confidence and competence of the students using AI tools. The techniques can also enable students to acquire critical thinking, teamwork, and practical problem-solving skills, which would be vital in their effective operation at the AI-assisted healthcare sites [15,19].
In addition, the findings indicate the necessity of including ethical, legal, and professional obligations related to AI in healthcare in the curriculum. As a way to balance and critically imagine the uses of AI, educators can make students more aware of the threat of job displacement, data privacy, and AI misuse.Such training not only equips studentswith the aspects of practical implementation of AI, but also to train their ability to recognize issues,maintain a cautious approach, and use AI in a safe and responsible way [12,16].
Overall, the present study shows that undergraduate students of nursing in Mardan, Northeast of Pakistanpossess a fairly good knowledge base on AI and tend to have a positive attitude towards the use of AI in the healthcare sector, which is moderated by ethical, safety, and employment issues.The paper highlights the critical role of the organized AI education, practical learning, and training in ethics to make sure that upcoming nurses will be prepared to apply AI efficiently and responsibly. It is possible to introduce AI to the nursing curriculum along with hands-on and interactive learning opportunities that will enable students to embrace the changing nature of healthcare technology, enhance patient care, and improve clinical decision-making [11,12,16,17,19].
Methodological Limitations
This research has certain methodological drawbacks. To start with, the participants were chosen through convenience sampling. Although this method was feasible in this exploratory study, it does not allow generalizing the results tothe entire undergraduate nursing student populationin the region. Second, despite the fact that the questionnaire used in the present research was based on already published tools,the questionnaire’s reliability (e.g.,Cronbach’salpha)was not evaluated in this study.Also, there was no formal cultural validation or cross-cultural adaptation of the toolor a fulldescription and reference of the original tool. Lastly, the students who volunteered to be on the platform might not have the same knowledge or attitudes as those who declined to doso,and this may create a self-selection bias.Subsequent studies should apply probabilistic sampling techniques, and the instruments must be fully valid and culturally adapted to enhance the accuracy and generalizability of the results.
CONCLUSION
This study concludes that undergraduate nursing students in nursing colleges of Mardan, Northeast of Pakistanpossess a good level of knowledge and generally positive attitudes toward the use of artificial intelligence (AI) in healthcare. Students demonstrated awareness of AI applications, potential benefits, and its role in improving healthcare delivery and clinical decision-making.
However, important concerns were also identified, particularly related to ethical issues, dataprivacy, legal responsibility, and job security. These findings indicate that although students are open to adopting AI technologies, their acceptance is influenced by fears and uncertainties regarding the safe and responsible use of AI in clinical practice.
The results highlight the urgent need for structured educational support through curriculum integration, practical training, and ethical guidance. Preparing future nurses to work effectively with AI requires not only technical knowledge but also an understanding of legal, professional, and ethical responsibilities.
Overall, this study emphasizes the importance of incorporating AI-related education into undergraduate nursing programs to ensure that future nurses are competent, confident, and ethically prepared to engage with emerging healthcare technologies.
Recommendations
Based on the findings of this study, the following recommendations are proposed to support the effective and responsible integration of artificial intelligence (AI) into nursing education and practice:
Integration of AI into Nursing Curriculum
Nursing education programs should formally incorporate AI-related content into undergraduate curricula. This should include basic concepts of artificial intelligence, its applications in healthcare, and its role in clinical decision-making. Early exposure will help students develop familiarity and confidence in using emerging technologies.
Emphasis on Ethical and Legal Education
Given the concerns expressed by students regarding privacy, legal responsibility, and ethical risks, nursing programs should strengthen education on ethical, legal, and professional issues related to AI. Teaching should focus on data protection, patient confidentiality, accountability, and safetechnology use in clinical settings.
Practical and Skill-Based Training
Educational institutions should provide hands-on learning opportunities such as simulations, workshops, and project-based learning involving AI-supported tools. Practical exposure can reduce fear, improve understanding, and enhance students’ readiness to work in technology-enabled healthcare environments.
Faculty Development and Training
Nursing educators should receive training on AI and digital health technologies to ensure effective teaching and guidance. Faculty preparedness is essential for successful curriculum implementation and for fostering a supportive learning environment.
Awareness Programs to Address Misconceptions
Seminars and awareness sessions should be conducted to address common fears such as job replacement and misuse of AI. Emphasis should be placed on the collaborative role of AI, highlighting that AI is designed to support healthcare professionals rather than replace them.
Policy and Institutional Support
Healthcare institutions and nursing regulatory bodies should develop clear policies and guidelines regarding the use of AI in clinical practice. This will help ensure safe implementation and build trust among future nurses.
Future Research
Further studies should be conducted using larger and more diverse samples across different regionsto improve generalizability.
Future research may also explore the effectiveness of AI education programs and interventions designed to improve students’ competencies and attitudes.
Local Ethics Committee approval
The research was carried out in line with the internationally agreed ethical principles of conducting research that involves human subjects. Advance ethical approval was received before the data collection to safeguard the rights, safety and well-being of the participants. The study research plan was checked and accepted by the Institutional Review Board (IRB) of Abdul Wali Khan University, Mardan.
IRB Title: Institutional Review Board, Abdul Wali Khan University Mardan.
IRB Number: [IRB/2025/AI-Nursing/0017]
Date of Approval: [15 March 2025]
The study was completely voluntary. All the undergraduate nursing students were made aware of the objective of the research,the methodpossible advantages and their right to opt out of the research at any given time without any repercussions regarding their academic performance or personal effects. All the participants were informed and provided written consent before data were collected.
No personally identifiable information was gathered to guarantee the confidentiality and anonymity. The coding of the questionnaires was done numerically, and all data was kept in a secure location and accessed by the research team alone. Data gathered had no other purposes than academic and research.The research, further, did not impose any physical, psychological, or academic harm on the respondents. The issue- knowledge and attitudes towards artificial intelligence in healthcare-was not sensitive and the participants could pass on any question that they feel uncomfortable to respond to.
Competing interests
The authors report no conflict of interest.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Authors' Contributions
The conceptualization and the study design was provided by Abdur Rahman and Muhammad Tariq. Ismail Shahid did the methodology development and design of the instruments. Data collection and field coordination was done by Khadija Bibi, Umair Islamand Mahnoor Ali. The data analysis and interpretation of results were done byRahim Shah, Arshad Ali, Noor Muhammad andShakir Ullah also managed the research, helped to refine the methodology, and provide the leadership of the manuscript writing, reviewing, and approval.
All authors approved the final version of the manuscript.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Nurse-led intervention on knowledge and awareness regarding chronic kidney disease among hypertensive and/or diabetic patients: A quasi-experimental study
Jyoti Jangid 1, Manju AK Rajora 2*, Rajiv Narang 3, Viveka P Jyotsna 4
- Continuing Nursing Education Cell, All India Institute of Medical Sciences, Jodhpur, India.
- College of Nursing, All India Institute of Medical Sciences, New Delhi, India.
- Department of cardiology, All India Institute of Medical Sciences, New Delhi, India.
- Department of endocrinology and metabolism, All India Institute of Medical Sciences, New Delhi, India.
* Corresponding author: Dr. Manju Amit Kumar Rajora, address- College of Nursing, All India Institute of Medical Sciences, New Delhi-110029, India. Contact no.: +919870260036; Email: manjuakrajora@aiims.edu
Cite this article
ABSTRACT
Background: Diabetes and hypertension are the leading causes of chronic kidney disease (CKD) worldwide, and adequate awareness is crucial for its prevention and early detection among high-risk populations.
Objective: To evaluate the effectiveness of a nurse-led educational program through a booklet on the awareness and knowledge of CKD among hypertensive and/or diabetic patients.
Methods: A pre-test and post-test control group design was used with a convenient sample of 90 patients, equally divided into the experimental and control groups, i.e., 45 in each. Awareness-knowledge was assessed using a validated self-structured questionnaire. The pretest was conducted in both groups, and the experimental group received a 25–30-minute education intervention. Post-test assessment was conducted after one month in both groups.
Results: The mean pre-test knowledge scores of patients in the experimental and control groups were 18.04± 6.47 and 17.42± 6.37, respectively. In the post-test, there was a significant increase in the knowledge score of patients in the experimental group (33.96± 4.59) compared to the control group (18.80± 5.55; p=0.001). Awareness of CKD was significantly associated with religion (p = 0.016), monthly income (p = 0.02) and duration of diabetes (p value= 0.04). In regression analysis, being widow/separated and earning under 10,000 INR per month were independently associated to lower knowledge scores, while education beyond high school was an independent positive predictor.
Conclusion: Nurse-led educational programs effectively enhance CKD knowledge, support self-management, and help prevent disease-related complications among hypertensive and/or diabetic patients.
Keywords: Chronic kidney disease, Diabetes, Hypertension, Knowledge, Nurse-led educational program.
INTRODUCTION
Chronic kidney disease (CKD) is an irreversible, progressive condition and a major global health burden, affecting nearly 1 in 10 individuals [1,2]. In 2017, CKD caused 1.2 million deaths, ranking as the 12th leading cause of death worldwide, with projections indicating it may rise to the 5th position by 2040 [3]. Alarmingly, about 90% of adults with CKD and 1 in 3 adults with severe CKD remain unaware of their condition, leading to delayed diagnosis and treatment and also increasing the burden on caregivers with a decrease in the quality of life of patients [4,5]. A systematic review reported that the prevalence of poor kidney function varies widely from 2.9% to 56% and confirmed CKD varied from 4.4% to 17.1% [6]. Risk factors differ across regions. In developed countries, ageing, diabetes, hypertension, cardiovascular diseases and obesity predominates, whereas in developing countries, infections, glomerular and tubulointerstitial diseases, and exposure to drugs and toxins are common causes [7–9]. Diabetes Mellitus (DM) and Hypertension (HTN) are the main causes of CKD worldwide [10–13].
Hypertension acts both as a risk factor by accelerating CKD progression and as a comorbidity contributing to cardiovascular mortality in CKD patients [12,14,15]. In India, a pilot study reported 70% of patients having advanced CKD stage 4-5, and Diabetes being the most common CKD, out of which 97% of cases were having type 2 diabetes [13,16]. Low awareness among high-risk populations contributes significantly to delayed diagnosis and poor outcomes [17–19]. Therefore, early risk stratification, screening, awareness, and education are essential strategies to slow CKD progression [20–22]. The studies have reported low awareness and knowledge regarding CKD among the high-risk population [17,23,24]. Global initiatives such as the National Health and Nutrition Examination Surveys and Kidney Early Evaluation Program for CKD emphasize early detection [25,26]. Health education combined with early screening empowers high-risk individuals to adopt healthy behaviors and effective self-management practices [25,27,28].
Objective
The objective of the study was to assess the awareness and knowledge of CKD among hypertensive and/or diabetic patients and to assess the effectiveness of an education booklet on knowledge of CKD among hypertensive and/or diabetic patients.
MATERIAL AND METHODS
The research hypothesised that a nurse-led education program would bring significant change in the knowledge of CKD among hypertensive and/or diabetic patients. The non-equivalent control group pre-post-test quasi-experimental design was employed for participants. The non-random, time-based allocation was adopted as a part of a quasi-experimental study design to minimise contamination between groups. Participants attending the cardiology OPD on Monday and endocrinology OPD on Tuesday were assigned to the control group, whereas those attending the cardiology OPD on Friday and endocrinology OPD on Thursday were assigned in experimental group. The data was collected from July 2018 to December 2018. A sample size of 44 was calculated in each group, based on the pilot study results. 90 patients were enrolled (45 per group), assuming a 90% power, 5% alpha error, and 10% attrition. The pre-test was administered to the participants in both the control and the experimental group which required approximately 10-15 minutes to complete. Data collection included demographic and clinical variables. The awareness regarding CKD was assessed by asking two questions of a yes/no type. First question (AQ1) enquired whether the patients were aware of their risk of developing CKD due to HTN and DM or not. Second question (AQ2) enquired whether they were informed by any health professional or not. There were 43 questions regarding knowledge of CKD, out of which 35 were yes/no type, and 8 were multiple choice questions. Each correct response was scored as ‘1’, and each incorrect response was scored as ‘0’. Knowledge level was categorized into poor knowledge (<18), average knowledge (18-26), good knowledge (26-35), and very good knowledge (>35). Content Validity was established by three nursing experts and two nephrologists. Reliability of the tools was assessed using the test-retest method (r=0.79) during pilot study on similar population. The tool was translated into Hindi, and reverse translation was done in English.
Inclusion Criteria
The participants aged above 18 years, diagnosed with HTN and/or DM for ≥ 6 months and visiting the cardiology and endocrinology outpatient department (OPD) for regular follow-up at a tertiary care hospital.
Exclusion Criteria
The participants with cognitive impairment and renal disease were excluded from the study.
Intervention
A registered nurse pursuing her postgraduate degree in nursing developed the education booklet under the guidance of study guides and experts.
The education booklet included information regarding kidneys, its functions, about CKD, its risk factors, signs and symptoms, preventive measures for diabetic and/or hypertensive patients, diagnostic investigation for CKD, its complications and the management. The education was given once to the participants of experimental group visiting the cardiology OPD (Friday) and endocrinology OPD (Thursday) for 25-30 minutes.
The post-test was carried out one month after the intervention in both the control and the experimental group. There was no loss to follow up and the data of all 45 participants in both groups were analysed.
Figure 1 illustrates the data collection process, including participant enrolment, group allocation to final analysis.
Figure 1. Flowchart of participants.
Local Ethics Committee approval and consent to participate
The study was approved by the institute's ethics committee for postgraduate research, AIIMS, New Delhi, Ref. No. IECPG-98/21.03.2018, and the study was approved on March 21, 2018. Eligible patients were informed, and written informed consent was taken; they were reassured of their confidentiality and autonomy. This study was conducted in accordance with the Declaration of Helsinki.
Statistical Analysis
STATA 14.0 was used for statistical analysis. The normal distribution of data was assessed using the Shapiro-Wilk Test. The reliability of the tool was assessed using the test-retest method. The degree of stability over time was evaluated using Pearson’s correlation coefficient (r), r > 0.7 was considered as a good correlation. Categorical variables were analysed using the Chi-square test and Fisher's exact test. Continuous variables following a normal distribution were analysed by the t-test; an unpaired t-test was used to compare the data between the control and experimental group, while a paired t-test was used to compare pre-test vs post-test data within the groups. The Wilcoxon rank-sum test was used to analyse data which was not distributed normally. Anova and Kruskal-Walli’s rank test was used to assess the relationship of pre-test knowledge score with categorical variables, and Spearman’s correlation coefficient was used to investigate the potential relationship between pre-test knowledge score with clinical variables. Univariable and stepwise multiple linear regression for calculating unadjusted and adjusted beta coefficients with 95% class interval were performed to find the independent association factors of knowledge. Categorical variables were included using dummy coding where one category serving as the reference group and assigned a value 0 like marital status (reference: unmarried), geographical region (reference: rural), educational level (reference: no formal education) and monthly income (reference: ≥40,000 INR) while the other categories converted into binary dummy variable (1 if present, 0 if absent). The level of significance was at p-value < 0.05.
RESULTS
The data were checked for homogeneity and were found comparable (p>0.05). Table 1 reports the demographic and clinical variable distribution of patients among the experimental and control groups. More than half (64%) of patients in the experimental group and (67%) patients in the control group, were aware about the risk of developing kidney disease due to HTN and/or DM (AQ1) and only 42% in experimental group and 36% in the control group got informed by any health care professional about their risk of developing CKD (AQ2).
| Variables | Experimental Group (n=45) | Control Group (n=45) | p-value (test) |
| Age (years) Mean ± SD (Range) |
51.71±13.71 (24-78) | 51.84±11.51 (18-66) | 0.96 (U) |
| n (%) | |||
| Gender Male Female |
26 (58) 19 (42) |
26 (58) 19 (42) |
0.99 (C) |
| Marital status Unmarried Married |
3 (7) 42 (93) |
2 (4) 39 (95.5) |
0.99 (F) |
| Occupation Government Job Private Job Health Professional Unemployed |
10 (22) 20 (45) 1 (2) 14 (31) |
7 (16) 21 (47) 0 (0) 17 (38) |
0.7 (F) |
| Residence Rural Urban |
12 (27) 33 (73) |
9 (20) 36 (80) |
0.46 (C) |
| Education Informal Primary High school Above High school |
10 (22) 10 (22) 12 (27) 13 (29) |
10 (22) 12 (27) 16 (36) 7 (16) |
0.50 (C) |
| Source of health education Hospital Health Education Program Other |
23 (51) 4 (9) 18 (40) |
24 (53) 2 (4) 19 (42) |
0.80 (F) |
| Monthly income (Rs.) >40,000 30,000-40,000 20,000-30,000 10,000-20,000 <10,000 |
4 (9) 10 (22) 17 (38) 10 (22) 4 (9) |
2 (4) 8 (18) 17 (38) 14 (31) 4 (9) |
0.80 (F) |
| Albuminuria § Nil Trace >1 |
14(66.7) 3(14.3) 4(19.1) |
15(65.2) 7(30.4) 1(4.4) |
0.22 (F) |
| Clinical Variables | Median (Range) | ||
| Duration of diabetes | 6.5(1-25) | 7(1-30) | 0.72 (W) |
| Duration of hypertension | 6(1-25) | 5.5(1-35) | 0.74 (W) |
| Serum Creatinine(mg/dl) | 0.9 (0.5-2) | 0.8 (0.4-1.3) | 0.02 (U) |
| GFR (1.73ml/min/m2) | 86 (32-208) | 94 (41-218) | 0.12 (U) |
Note: § Albuminuria report of only 21 patients in the experimental group and 23 patients in the control group was available. U (Unpaired T-test), C (Chi-Square Test), F (Fisher’s Exact Test), W (Wilcoxon rank-sum Test)
Table 1. Distribution of demographic and clinical variables of patients of the experimental and control groups.
The knowledge level assessed at baseline showed that 44.4% patients in the experimental and 51.1% in control group had poor knowledge, 44.4% in the experimental and 37.7% in the control group had average knowledge, 9% patients in the experimental and 11% in the control group had good knowledge; however, only 2.2% patients in the experimental group had very good knowledge, and none in the control group had very good knowledge. Table 2 showed that at baseline, both groups were similar in knowledge level and the nurse-led education program was effective in improving knowledge of CKD among hypertensive and/or diabetic patients.
| Groups | Pre-test Score Mean± SD (Min-Max) |
Post-test Score Mean± SD (Min-Max) |
p-value (test) |
| Experimental group (n=45) | 18.04 ± 6.47 (4-35) | 33.96 ± 4.59 (21-43) | 0.001* (P) |
| Control group (n=45) | 17.42 ± 6.37 (3-27) | 18.80 ± 5.55 (9-30) | 0.0018* (P) |
| p-value | 0.65 (U) | 0.001* (U) |
Note: * (significant test), U (Unpaired T-test), P (Paired T-test).
Table 2. Comparison between the knowledge score of the experimental and control groups.
Table 3 shows that after the nurse-led educational program, in the post-test, the experimental groups showed a greater improvement in knowledge scores (diabetics p = 0.001, hypertensives p = 0.001, and hypertensive-diabetics p = 0.001) compared to the control group (diabetics p = 0.17, hypertensives p = 0.12, and hypertensive-diabetics p = 0.10), further emphasizing the effectiveness of the intervention even at the subgroup level.
| Knowledge score | Experimental group (n=45) | Control group (n=45) | ||
| Diabetic (n=15) | Pre-test score | 17.40±4.50 | 16.66±5.99 | |
| Post-test score | 33.80±2.95 | 18.13±4.43 | ||
| p-value (test) | 0.001* (P) | 0.17 (P) | ||
| Hypertensive (n=15) | Pre-test score | 18.66±6.87 | 16.93±7.45 | |
| Post-test score | 34.20±5.64 | 17.93±6.09 | ||
| p-value (test) | 0.001* (P) | 0.12 (P) | ||
| Diabetic and hypertensive (n=15) | Pre-test score | 18.06±7.95 | 18.66±5.77 | |
| Post-test score | 33.86±5.06 | 20.33±5.99 | ||
| p-value (test) | 0.001* (P) | 0.10 (P) | ||
Note: * (significant test), P (Paired T-test).
Table 3. Comparison of knowledge score between sub-groups of experimental and control group.
Table 4 reported the relationship between awareness and demographic variables. Patients with a higher monthly income (p = 0.02), Hindu by religion (p = 0.01), showed greater awareness of the risk of chronic kidney disease.
| Variables | AQ1 | AQ2 | |||||
| NO n(%) |
YES n(%) |
p-value (test) | NO n(%) |
YES n(%) |
p-value (test) | ||
| Age(years) | Mean ± SD | 55.35±12.38 | 49.89±12.39 | 0.05(U) | 53.40±12.56 | 49.22±12.39 | 0.12(U) |
| Gender | Male | 20 (64.5) | 32 (54.2) | 0.37(F) | 32 (58.2) | 20 (57.1) | 0.96 (F) |
| Female | 11 (35.5) | 27 (45.8) | 23 (41.8) | 15 (42.9) | |||
| Religion | Hindu | 29(93.6) | 46 (78) | 0.01* (F) | 47 (85.5) | 28 (80) | 0.39(F) |
| Muslim | 0 (0) | 10 (17) | 4 (7.3) | 6 (17.1) | |||
| Sikh | 2 (6.4) | 1(1.7) | 2 (3.6) | 1 (33.3) | |||
| Christian | 0 (0) | 2(3.4) | 2 (3.6) | 0 (0) | |||
| Marital status | Unmarried | 1 (3.2) | 4 (12.9) | 0.40(F) | 3 (5.4) | 2 (5.7) | 0.99(F) |
| Married | 25 (80.6) | 51 (86.4) | 46 (83.6) | 30 (85.7) | |||
| Widow/widowed | 1 (3.2) | 0 (0) | 1 (1.8) | 0 (0) | |||
| Separated | 4 (12.9) | 4 (12.9) | 5 (9.1) | 3 (8.6) | |||
| Occupation | Government Job | 6 (19.4) | 11(18.6) | 0.80(F) | 12 (21.8) | 5 (14.3) | 0.42(F) |
| Private Job | 16(51.6) | 25 (42.4) | 26 (47.3) | 15 (42.9) | |||
| Health Professional | 0 (0) | 1 (1.7) | 0 (0) | 1 (2.9) | |||
| Unemployed | 9 (29.0) | 22 (37.3) | 17 (30.9) | 14 (40) | |||
| Geographical region | Urban | 8 (25.8) | 13 (22) | 0.68(C) | 14 (25.5) | 7 (20) | 0.55(C) |
| Rural | 23 (74.2) | 46 (78) | 41 (74.5) | 28 (80) | |||
| Education | Informal Education | 9 (29.0) | 11 (35.5) | 0.07(F) | 12 (21.8) | 8 (22.9) | 0.52(F) |
| Primary Education | 11(35.5) | 11(35.5) | 13 (23.6) | 9 (25.7) | |||
| High school | 5 (16.1) | 23 (39) | 15(27.27) | 13 (37.1) | |||
| >High school | 6 (19.4) | 14 (23.7) | 15(27.27) | 5 (14.3) | |||
| Source of health education | Hospital | 17 (54.8) | 30 (50.9) | 0.93(F) | 26(47.27) | 21 (60) | 0.31(F) |
| Health Edu. Prog. | 2 (6.5) | 4 (6.8) | 3(5.45) | 3 (8.6) | |||
| Other (specify) | 12 (38.7) | 25 (42.4) | 26(47.27) | 11(31.4) | |||
| Monthly income(Rs.) | >40,000 | 1 (3.2) | 5 (8.5) | 0.02*(F) | 1(1.82) | 5 (14.3) | 0.05(F) |
| 30,000-40,000 | 5 (16.1) | 13 (22.0) | 12(21.82) | 6 (17.1) | |||
| 20,000-30,000 | 10 (32.3) | 24 (40.7) | 18(32.73) | 16 (45.7) | |||
| 10,000-20,000 | 8 (25.8) | 16 (27.1) | 17(30.91) | 7 (20) | |||
| <10,000 | 7 (22.6) | 1 (1.7) | 7(12.73) | 1 (2.9) | |||
Note: * (significant test), (U) t-test, (C) Chi square, (F) generalised Fisher’s Exact Test, (W) Wilcoxon test.
Table 4. Relationship between Awareness and Demographic Variables.
Table 5 reported the relationship between awareness and clinical variables and found that patients having diabetes for a longer period of time had higher awareness of CKD risk (p=0.04).
| Clinical Variables | AQ1 | AQ2 | |||||
| NO | YES | p-value | NO | YES | p-value (test) | ||
| Duration of diabetes (Median) | 4 | 8.5 | 0.04*(W) | 5.5 | 9.5 | 0.06(W) | |
| Duration of hypertension (Median) | 6 | 6 | 0.38(W) | 6 | 5 | 0.74(W) | |
| Albuminuria | Nil | 12 | 17 | 0.18 (F) | 19 | 10 | 0.46 (F) |
| Trace | 1 | 9 | 5 | 5 | |||
| >1 | 1 | 4 | 2 | 3 | |||
| Serum Creatinine(mg/dl) | 0.9 | 0.8 | 0.13(W) | 0.87 | 0.9 | 0.64(W) | |
| GFR (1.73ml/min/m2) | 86 | 95 | 0.18(U) | 0.90(U) | |||
Note: * (significant test), U (Unpaired t-test), C (Chi-square), F (generalised Fisher’s Exact Test), W (Wilcoxon test).
Table 5. Relationship between Awareness and Clinical Variables
Table 6 showed the relationships between demographic variables and knowledge score. Patients living in urban areas (p=0.03), unmarried (p=0.008), with more than high school education (p=0.0008), and a monthly income of 30-40 thousand rupees (p=0.01) had higher knowledge than others.
| Demographic Variables | Knowledge Score (Mean ± SD) | p-value (test) | |
| Gender Male Female |
17.76 ± 6.83 17.68 ± 5.82 |
0.95 (U) | |
| Religion Hindu Muslim |
17.45 ± 6.65 19.13 ± 4.82 |
0.35 (U) | |
| Marital status Unmarried Married Widow/widowed/Separated |
24.60 ± 3.20 17.76 ± 6.15 13.66 ± 6.83 |
0.008* (K) | |
| Occupation Government Job/Health Professional Private Job Unemployed |
19.44 ± 8.51 17.68 ± 6.02 16.80 ± 5.39 |
0.38 (A) | |
| Geographical region Rural Urban |
15.09 ± 6.96 18.53 ± 6.03 |
0.03* (U) | |
| Education Informal Education Primary Education High school >High school |
13.90 ± 4.96 16.18 ± 5.43 19.32 ± 5.35 21.05 ± 7.74 |
0.0008* (A) | |
| Source of health education Hospital Health Edu. Prog. Other (specify) |
17.10 ± 6.68 21.66 ± 5.04 17.89 ± 6.10 |
0.23 (K) | |
| Monthly income (INR) >40,000 30,000-40,000 20,000-30,000 10,000-20,000 <10,000 |
19.67 ± 9.69 20.77 ± 6.50 17.20 ± 5.79 17.79 ± 5.04 11.50 ± 5.90 |
0.01* (K) | |
| Albuminuria Nil Trace > +1 |
17.10 ± 4.95 19.50 ± 4.57 19.20 ± 9.17 |
0.47 (K) | |
Note: * (significant test), U (Unpaired T-test), A (Anova), K (Kruskal-Wallis rank test).
Table 6. Relationship of pre-test knowledge score with selected variables.
In Table 7, no correlation was found between knowledge and clinical variables (age, duration of diabetes, duration of hypertension, serum creatinine(mg/dl), and GFR (1.73ml/min/m2)).
| Clinical Variables | Spearman’s Coefficient (rho) | p-value (test) |
| knowledge / Age | -0.165 | 0.12 (S) |
| knowledge / Duration of diabetes | 0.049 | 0.70 (S) |
| knowledge / Duration of hypertension | 0.002 | 0.99 (S) |
| knowledge / Serum Creatinine(mg/dl) | -0.067 | 0.52 (S) |
| knowledge / GFR (1.73ml/min/m2) | 0.08 | 0.41 (S) |
Table 7. Correlation analysis between Knowledge score and Clinical Variables.
In Table 8, the variables that were statistically significant in bivariate analysis (Table 6) were included in univariable and multiple linear regression analysis. In the adjusted stepwise multiple linear regression model, being widowed/separated and having a monthly income of less than 10,000 INR remained independently associated with lower knowledge scores, while education beyond high school emerged as an independent positive predictor. Other variables did not retain statistical significance after adjustment. The results were interpreted as the knowledge among widowed/separated patients was less as compared to unmarried patients.
| Variables | Unadjusted beta coefficient with 95% CI | p-value | Step-wise linear regression | p-value |
| Marital status Married Widow/widowed/Separated |
-6.83 (-12.4, -1.2) -10.93 (-17.7, -4.2) |
0.018 0.002 |
-5.11 (-10.2, 0.04) -7.90 (-14.2, -1.6) |
0.05 0.015 |
| Residence Urban |
3.44 (0.3, 6.5) | 0.03 | ______ | _____ |
| Education Primary High school >High school |
2.28 (-1.3, 5.9) 5.42 (1.9, 8.8) 7.15 (3.4, 10.8) |
0.21 0.002 0.001 |
1.93 (-1.5, 5.4) 4.48 (1.2, 7.8) 6.06 (2.5, 9.6) |
0.26 0.008 0.001 |
| Monthly income 30,000-40,000 20,000-30,000 10,000-20,000 <10,000 |
1.11 (-4.5, 6.7) -2.46 (-7.8, 2.8) -1.87 (-7.3, 3.6) -8.16 (-14.6, -1.6) |
0.69 0.36 0.50 0.01 |
-0.44 (-5.6, 4.7) -3.06 (-7.9, 1.8) -2.20 (-7.2, 2.8) -7.79 (-13.7, -1.8) |
0.86 0.21 0.38 0.011 |
Table 8. Regression analysis of knowledge with selected variables.
A significant increase in knowledge was found in patients who had education up to high school and beyond high school, respectively, as compared to patients who had informal education. There was a significant decrease in knowledge score in patients who had a monthly income of less than 10,000 rupees compared to patients who had monthly income more than 40,000 rupees.
DISCUSSION
In the present study, 65.5% were aware of the risk of kidney disease in hypertensive and/or diabetic patients. Similarly, 60.6% respondents recognised diabetes as a risk factor for renal disease [29]. In the present study, 44.4% in the experimental and 51.1% in the control group had poor knowledge, 44.4% in the experimental and 37.7 in the control had average knowledge, 9% in experimental and 11.1% in control group had good knowledge, 2.2% in experimental and none in control had very good knowledge. Nearly the same, 55% of participants had average knowledge regarding renal disease [30]. In our study the knowledge score was significantly improved pre-test 18.04 ± 6.47 to post-test 33.96±4.59 at p=0.001in the experimental group similarly there was significant increase in knowledge of CKD was reported (p < 0.05) [31,32]. Knowledge was higher in unmarried subjects, living in an urban region, having an education up to or more than high school, and having a monthly income of more than 30,000 rupees. Similarly, patients with higher education had more knowledge of renal disease than those patients who had lower education (p=0.001) [10,24,34]. Patients having lower income <$ 2000 [Odds ratio (OR) 0.41, 95% class interval (CI)] and lower education (OR 0.33. 95% CI) had poor knowledge score of CKD [30,35].
The post-test was taken after one month of the intervention, rather than immediately, which could affect the novelty effect, causing a threat to external validity and no attrition at follow-up was a strength of the study.
Limitations
Awareness was assessed using two questions and most items in the knowledge questionnaire were closed-ended in nature and may overestimate the knowledge or limit the critical ability of critical reasoning related to kidney health.
The study didn’t evaluate the gain translated into sustained behavioural changes, treatment adherence, or improved clinical outcome. Additionally, the single-centre, quasi-experimental design with convenient sampling and lack of randomization may impact the external validity, limit the causal inference and generalizability.
CONCLUSION
In conclusion, the nurse-led intervention significantly improved the CKD knowledge score among hypertensive and/or diabetic patients. Appropriate information empowers hypertensive and/or diabetic patients to manage better blood pressure, blood sugar, and lifestyle changes, potentially reducing the risk and progression of kidney disease. Multicentric studies are needed, along with structured nurse-led education and counselling programs for these patients, and longitudinal research to comprehensively evaluate kidney health maintenance.
List of abbreviations
CKD: Chronic Kidney Disease
HTN: Hypertension
DM: Diabetes Mellitus
OPD: Outpatient Department
AQ1: Awareness Question 1
AQ2: Awareness Question 2
AIIMS: All India Institute of Medical Sciences
IECPG: Institute Ethics Committee for Postgraduate
STATA: Statistics and Data Analysis software
GFR: Glomerular Filtration Rate
OR: Odds Ratio
CI: Class Interval
Funding
The study was not funded by any public, private, commercial and non-profit sector.
Conflicts of interest
The authors declare that there is no conflict of interest.
Author contributions
Conceptualisation: JJ, MAKR, RN, VPJ, methodology: JJ, MAKR, RN, VPJ, Software: JJ, Data Collection: JJ, MAKR, RN, VPJ, Data analysis and interpretation: JJ, MAKR, writing- original draft preparation: JJ, MAKR, writing-review and editing: JJ, MAKR, supervision: MAKR, RN, VPN
Acknowledgement
We acknowledge the patient participation in the study and department of Biostatistics for statistical analysis.
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LOCAL EXPERIENCE OF COORDINATION IN ROBOTIC SURGERY: ORGANIZATIONAL REFLECTIONS
Rita Citarella 1*, Marco Abagnale 2
- Department of Surgery and Anesthesia, “Umberto I” Hospital of Nocera Inferiore, 84014, Salerno, Italy.
- Department of Critical Care, M. Scarlato Hospital, 84018, Scafati, Salerno, Italy.
* Corresponding author: Rita Citarella, Department of Surgery and Anesthesia at Umberto I Hospital of Nocera Inferiore, 84014 Salerno, Italy. E-mail: rita.citarella.91@gmail.com
Cite this article
ABSTRACT
Introduction: The launch of the robotic surgery program in our hospital showed that the main challenges did not concern the technology itself but rather the organization of work. Delays in operating room preparation, unclear task distribution, fragmented communication among professionals, and inconsistent management of instrument traceability highlighted the absence of a clearly defined coordination function.
Methods: Through descriptive observations drawn from daily activity during the initial phases of the program, operational episodes, team dynamics, and workflow patterns were recorded in order to understand how the system adapted to the introduction of the robotic platform.
Results: From these observations, the figure of the “Da Vinci Coordinator” (DVC) emerged locally as a practical response to organizational challenges. This function contributed to aligning tasks among teams, making workflow preparation more predictable, improving interprofessional communication, and supporting internal training activities. The few descriptive indicators included served solely to contextualize the experience.
Conclusion: The DVC function was not conceived as a formalized or generalizable professional role, but rather as an emergent organizational adaptation useful during the implementation phase of a robotic program. The considerations presented may offer insights for other centers preparing to introduce robotic surgery; however, further structured studies will be necessary to assess its transferability to different contexts.
Keywords: Robotic surgery; perioperative coordination; organisational role; instrument traceability; Team integration; Da Vinci system
INTRODUCTION
Robotic surgery is increasingly used to support minimally invasive procedures, with well‑documented advantages in precision, patient safety, length of stay, and postoperative recovery [1–5]. In January 2025, the Umberto I Hospital of Nocera Inferiore introduced the Da Vinci system within the ASL Salerno network. Although robotic platforms are typically associated with technological benefits, our early implementation phase highlighted challenges of a different nature: the most recurrent difficulties were organizational rather than technical. During the first weeks, we observed delays in operating room start times, unclear task allocation during system preparation, fragmented communication among surgical, anesthesiology, nursing, and technical staff, and inconsistent procedures for instrument traceability and expiry control. The lack of a standardized monitoring protocol also resulted in occasions where robotic instruments exceeded their prescribed service life without timely identification, creating risks of unavailability or malfunction [6]. These observations underscored a key insight: in the start‑up phase of a robotic program, patient safety and workflow stability depend not only on technology or surgical skill, but also on a clearly defined coordination function capable of integrating clinical, technical, and organizational activities across the perioperative pathway [7]. To address these gaps, our center introduced the Da Vinci Coordinator (DVC), conceptualized as a coordination function rather than a formal professional role and assigned to an experienced operating room nurse trained on the robotic system. The role emerged as a practical response to early challenges and was maintained as staff increasingly recognized its value for workflow predictability, interprofessional communication, and training support.
Objective
The purpose of this commentary is to explain why this coordination function became necessary during the early implementation phase, to describe its main activities, and to reflect on how this experience may support other centers preparing to introduce robotic surgery.
Rationale behind the introduction of the Da Vinci Coordinator
During the first weeks of using the robotic system, our local experience revealed a recurrent organizational need that may be relevant for other centres starting a robotic programme [8]. In response, the hospital established a multidisciplinary working group including surgeons, anesthesiologists, and operating room nurses to review early operational episodes and identify practical priorities. These discussions motivated the introduction of the Da Vinci Coordinator (DVC), assigned in our unit to a single operating room nurse with advanced competencies and specific training on the robotic system and its instruments. Initially introduced as a pragmatic solution to early‑stage challenges, the DVC function was subsequently maintained as staff perceived clear benefits in workflow predictability, standardization of preparation, and interprofessional collaboration elements that are critical for supporting safe, patient‑centered care during the start‑up of a robotic program. In our setting, the essential activities observed included (Figure 1):
Figure 1. Key competence that may have the Da Vinci Coordinator.
(1) Technical supervision of system readiness and troubleshooting; (2) Organizational coordination to align workflow and responsibilities; (3) Interprofessional training supporting the team’s learning process. These activities reflect a context‑dependent coordination function rather than a formal professional standard, illustrating how dedicated coordination mechanisms may be essential during early robotic implementation. In our setting, the DVC was conceptualized as a distinct coordination function compared with the standard operating room nurse [9].
Clinical, technical, and organizational skills (locally observed)
In the context of our start-up phase, the DVC role integrated clinical competencies (procedure-specific patient positioning), technical competencies (system readiness verification, troubleshooting and escalation pathways, instrument traceability, and management of usage life and expiry), and organizational competencies (workflow preparation, clarification of professional roles, facilitation of multidisciplinary communication, and provision of training support). These competencies are presented as context-dependent observations derived from a single-center implementation phase and do not constitute a formally codified professional standard (Table 1).
| Aspect | Traditional OR Nurse [10] | Da Vinci Coordinator (DVC) [Fig. 2] |
| Role focus | Intraoperative assistance | Coordination across the robotic surgical pathway |
| Competence | Primarily clinical intraoperative skills | Integrated clinical, technical, and organizational skills |
| Instrument management | Basic instrument control | Traceability, usage-life checks, system readiness verification |
| Team interaction | Interaction with surgeons, anesthesiologists, OR nurses and technical/support staff) | Cross-team communication among the same professional groups |
| Training role | Limited or none | Support to onboarding and standardized setup routines |
| Responsibility procedure-based | Focused on the current procedure | Coordination of preparation and workflow across sessions (context-dependent) |
Table 1. Preliminary, locally observed functional comparison between the traditional OR nurse role and the coordination function referred to as “Da Vinci Coordinator (DVC)” in our setting.
Core skills and tasks of the Da Vinci Coordinator (locally observed)
In our setting, the DVC combines clinical, technical, and organisational support (Table 1; Figure 2). Rather than providing a procedural checklist, we summarise the DVC contribution as a coordination function across perioperative phases, aimed at reducing variability and making interdependencies manageable during the start-up period.
Across phases, three recurrent coordination mechanisms were observed:
- Before surgery: aligning timing and responsibilities; verifying instrument readiness and traceability/usage-life; ensuring basic system readiness.
- During surgery (setup/docking): facilitating bidirectional communication among teams; supporting standardised setup routines; coordinating escalation when technical or workflow disruptions occur.
- After surgery updating traceability records; capturing causes of start-time deviations when present; enabling rapid readiness for subsequent sessions and brief feedback for iterative learning.
These activities are reported as context-dependent observations from a single-centre implementation phase and are not proposed as a formal professional standard.
The Da Vinci Coordinator clinical insights: minimum indicators
To contextualise this local experience, we report a small set of descriptive observations from the start‑up phase (March–November 2025; 75 procedures). These elements are not intended as an assessment of effectiveness but solely to frame the coordination perspective discussed in this commentary
- Instrument governance: no episodes of instruments exceeding service life or requiring unplanned traceability checks.
- Start‑time predictability: two delays of 15 minutes, both linked to lower scrub‑nurse familiarity with robotic instrumentation.
- Team coordination: clearer role allocation, more reliable communication, faster instrument retrieval, and better adaptability to workflow changes.
- Training: three full days of standardized training for the dedicated robotic nursing team.
Together, these elements suggest that coordination activities may contribute to improving workflow stability during early implementation (Figure 2).
Figure 2. Descriptive representation of coordination interfaces in our setting: not a codified organizational model
Coordinating activities across perioperative phases (locally observed)
- Before surgery (preoperative / pre-session), the following activities should be undertaken: verification of the planned robotic procedure requirements; confirmation of instrument availability, remaining usage-life/expiry, and updating of instrument traceability; coordination of instrument retrieval from multiple storage locations (operating room supply, pharmacy); confirmation of the patient positioning strategy and required positioning accessories; performance and/or coordination of system readiness checks (surgeon console, patient cart, vision cart); and alignment of timing, team roles, and setup responsibilities through a structured pre-session briefing and/or checklist.
- During surgery (setup, docking, intraoperative support), key responsibilities include: facilitation of real-time, bidirectional communication among surgical, anesthetic, nursing, and technical personnel; support of standardized setup and docking protocols; management of unanticipated requirements (e.g., rapid instrument retrieval, instrument substitutions, or workflow modifications); escalation and coordination of technical troubleshooting to minimize procedural interruptions; and support of rapid adaptation when novel techniques or intraoperative changes necessitate modifications in patient positioning or workflow organization.
- After surgery (post-session), the following measures should be completed: documentation of instrument utilization and corresponding updates to traceability records, including notation of any device- or process-related issues; recording of start-time deviations and their underlying causes, when applicable; planning and coordination of restocking for all consumed or opened materials to ensure readiness for subsequent procedures; and systematic capture of concise feedback and lessons learned to promote continuous quality improvement during the implementation and maturation of the robotic surgery program.
Taking care of the patient
The initial phase of robotic surgical is when the DVC collaborates with surgeons, anesthesiologists, and operating room nursing staff to conduct a comprehensive preoperative evaluation and systematically coordinate the patient’s subsequent surgical pathway [10]: reducing uncertainty, aligning roles, and preventing delays during the initiation phase of robotic surgery.[11]; As a Da Vinci Coordinator, taking care of the patient means ensuring safety, precision, and comfort at every stage of robotic surgery. This is managed through simple operational tools: a pre-session readiness checklist (system check, instrument availability/usage-life/expiry, positioning plan) [12], a short team briefing to align roles and timing, and an instrument traceability log updated at the end of each session; when issues are identified, they are communicated through structured alerts to the appropriate t contacts [13].
Future directions
Our experience suggests that a robotic surgery program can run better when there is a simple and clear way to coordinate the work. However, our data come from one center only (March–November 2025, 75 procedures), so we propose only realistic and small improvements.
Based on what we observed (instrument control, start-time delays, communication, and training needs), we suggest four practical directions:
- Add coordination to routine session planning. Include the DVC in weekly or monthly planning of robotic sessions, instrument availability checks, and short pre-session readiness steps. Use simple checklists/logs to document key actions without creating extra bureaucracy.
- Create training focused on start-up problems. Develop short training modules based on real issues seen during early use (instrument checks and traceability, standardized setup, communication during docking, and adaptation to new procedures). Test these modules locally or within regional networks.
- Use basic digital tools to support instrument governance. Start with simple digital tracking (alerts for usage limits/expiry, replacement planning, and a basic delay log). Consider advanced analytics after data collection becomes stable and reliable.
- State the role clearly and define its limits. The DVC is not a ward/unit coordinator and does not manage staffing or overall department organization. The DVC is a procedure-focused coordination function, limited to the robotic pathway (pre-session preparation, setup support, instrument governance, communication, and training support). Clear boundaries help avoid overlap and confusion.
Overall, this commentary does not propose a standardized professional model. It offers a practical coordination perspective and a small set of process measures that other centers can use when starting a robotic program.
DISCUSSION
Our early implementation experience showed that the main sources of variability and delay were not related to the robotic technology itself, but to gaps in coordination and governance. Introducing a dedicated coordination function made these interdependencies visible and manageable across teams and phases. From this experience, three practical lessons emerged:
- the need to define early “who does what” and establish clear instrument governance;
- the value of brief, structured communication routines during setup;
- the importance of training focused on the most frequent start‑up challenges, including setup routines, instrument management, and communication during preparation and docking.
These observations are consistent with the literature showing that robotic implementation requires not only technological investment but also structured coordination, communication, and workflow standardisation [8, 11–13]. Reports on robotic nurse specialists or perioperative robotic coordinators also suggest that responsibilities vary across centres and that no single standard model exists [8, 11–13]. Our commentary adds to this literature by offering a practical “coordination lens” for the start-up phase and a small set of feasible process indicators that can support reflection and future evaluation. Describing the DVC as a function (not a fixed job title) allows each hospital to adapt it to its own context and to choose a few simple measures to monitor progress.
Limitations
This commentary is based on a single‑centre start‑up experience and does not aim to demonstrate effectiveness. The indicators reported are minimal and primarily process‑based, and several observations remain qualitative and may reflect local perceptions. Data were not collected through a predefined structured protocol, and the absence of a formal comparative evaluation limits interpretability. Findings may also be influenced by learning curves, case mix, and team experience or turnover. For these reasons, the DVC should be interpreted as a context‑dependent coordination function intended to stimulate reflection rather than a validated or universally generalizable model.
CONCLUSION
Introducing robotic surgery requires not only technology and technical skills but also clear coordination work. Describing the DVC as a practical coordination function, rather than a fixed job title, allows each hospital to adapt it to its own organizational structure and to monitor a few simple process indicators to assess whether daily work is becoming more stable as the robotic program evolves.
Local Ethics Committee approval
Not applicable. This is a commentary reporting only aggregated, non-identifiable process information; no patient-level data were collected.
Conflict of interest
The authors report no conflict of interest.
Funding
No specific funding was received for this work.
Authors’ contribution
RC and MA were the only two contributors in writing the manuscript. RC and MA discussed the importance of the Da Vinci Coordinator role during a work meeting and decided to report and discuss this local coordination experience. Both authors contributed equally to the conception and writing of the manuscript.
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Competencies of Nursing Tutors in Clinical Training: A Nationwide Italian Survey Protocol
Gian Domenico Giusti 1,2,*, Alessio Gili 3, Stefano Bambi 4, Yari Longobucco 4, Rocco Mazzotta 2,5
- Education and Quality Unit, Bachelor’s Degree Program in Nursing, Perugia University Hospital, Perugia, Italy
- Department of Biomedicine and Prevention, University of Tor Vergata, Rome, Italy.
- Department of Life Sciences, Health and Health Professions, Link Campus University Rome, Italy.
- Department of Health Sciences, University of Florence, Florence, Italy.
- Center of Excellence for Nursing Culture and Research, Order of Nursing Professions of Rome, Italy.
*Corresponding author: Gian Domenico, Giusti, Department of Biomedicine and Prevention, University of Tor Vergata, Rome, Italy. E-mail: giandomenico.giusti@students.uniroma2.eu ORCID: https://orcid.org/0000-0001-9167-9845
Cite this article
ABSTRACT
Introduction: Clinical mentoring is essential for nursing education. It facilitates the integration of theory and practice, while promoting the development of clinical, communication, and interpersonal skills. It is becoming increasingly evident that tutors are facing a number of challenges. These challenges are related to the cultural diversity of students and constantly evolving clinical contexts. Despite the emphasis placed on the significance of general and cultural competencies in mentors within the context of international literature, a paucity of studies in Italy exists that evaluate both dimensions employing standardised and validated tools.
Objective: This protocol describes a nationwide survey that will assess the mentoring and cultural competencies of Italian nursing tutors.
Methods: The study adopts a descriptive cross-sectional observational design, with convenience sampling of approximately 600 tutors active in the academic year 2024–2025. The collection of data will be conducted between July and December 2025 through the utilisation of digital questionnaires. The survey employs two instruments, namely the Mentors' Competence Instrument (MCI) and the Mentors' Cultural Competence Instrument (MCCI), in order to assess these competencies. The MCI has been developed to measure tutors' general skills, including pedagogical, relational and feedback dimensions, while the MCCI has been developed to assess cultural skills and intercultural communication. The collection of sociodemographic data, contextual information and open-ended responses pertaining to the strengths and weaknesses of the tutorial role will also be undertaken. Statistical procedures will include descriptive analyses, using tables and plots to represent the data, as well as inferential analyses such as the Chi-square test, t-test, ANOVA test, Mann–Whitney U test, Kruskal–Wallis test, and correlation analysis. All statistical tests with p < 0.05 will be considered statistically significant.
Results: The results of the study will inform the development of targeted training interventions and organisational strategies to enhance the role of the clinical tutor.
Conclusions: The survey will contribute to the enhancement of mentoring quality and the professional development of tutors, thereby facilitating the strengthening of the effectiveness of clinical training programmes in Italy.
Keywords: mentoring, nursing tutor, cultural skills, survey, clinical training.
INTRODUCTION
Clinical mentoring is a fundamental pillar of nursing education. It guides students in integrating theory and practice, while also promoting the development of clinical, communication, and interpersonal skills [1]. Changes in healthcare contexts and the increasing cultural and linguistic diversity of students mean that the role of the tutor is becoming more complex and important [2]. International literature shows that effective tutoring improves student satisfaction, reduces anxiety, and contributes to patient safety [3]. Furthermore, Directive 2013/55/EU [4] stipulates that at least 50% of nursing training must take place in clinical settings, thereby expanding the responsibilities of tutors and necessitating their adequate preparation.
In recent years, research has further defined the multidimensional nature of the skills required of tutors [5]. Pramila-Savukoski et al. [6] have emphasised that these skills encompass pedagogical, organisational, and professional elements. In this context, the Mentors' Competence Instrument (MCI), which was developed and validated by Tuomikoski et al. [7], is a standardised self-assessment tool that is widely used to evaluate the general abilities of nursing tutors. This evaluation covers areas such as pedagogical knowledge, relationships between mentors and students, providing feedback, and assessing students. Added to this is cultural competence, which is essential in supporting students from different backgrounds. Luukkonen et al.'s [8] study showed that, despite having moderate levels of cultural awareness, tutors demonstrate gaps in their practical interaction skills and cultural confidence, with variability related to age, experience, and the frequency with which they deliver tutorials.
A key contribution to this field is the work of Oikarainen et al. [9], who developed and validated the Mentors’ Cultural Competence Instrument (MCCI). This is now considered one of the most comprehensive tools for measuring the cultural competence of tutors. Meanwhile, Giusti and Mazzotta [10] emphasised the pivotal role of tutors in fostering cultural competence among nursing students and facilitating the establishment of inclusive clinical environments.
Further evidence at the European level comes from a multicentre study by Mikkonen et al. [11], which identified three distinct profiles of tutorial competence among over 1,600 mentors from five European countries. The study showed that professional experience, specific training, and the frequency with which mentors undertake tutoring activities are correlated with higher levels of competence. Italian mentors, however, scored lower in several areas, including providing constructive feedback, adopting a goal-oriented approach to mentoring, and engaging in critical reflection [11], these results suggest the need for dedicated training programmes in an Italian context. Meanwhile, a recent systematic review by Keinänen et al. [12] confirmed that educational interventions aimed at tutors can significantly improve skills such as student assessment, providing feedback, setting goals, and developing professional self-efficacy. The analysed interventions, which were often based on blended methods, demonstrated consistent and significant improvements in various dimensions of tutorial competence over time [12].
In nursing education literature, the term mentor is often used interchangeably with other roles such as facilitator, peer instructor, preceptor, clinical guide, clinical instructor, or supervisor [7]. In this context, a mentor can be defined as a registered nurse who supports undergraduate nursing students in their learning process and is responsible for teaching and assessing students during clinical practice, without being an employee of the educational institution. Mentoring takes place within the clinical learning environment, which encompasses not only the physical setting but also psychosocial and interactional factors, organisational culture, and teaching and learning components that can strongly influence students’ learning experiences.
In the Italian context, these mentoring functions are often carried out within tutoring activities, reflecting a partial overlap between the roles of mentor and tutor in clinical education.
However, the national literature shows that there are not many studies that look at the general and cultural skills of Italian nursing tutors using standard tools like the MCI and MCCI.
Objective
The aim of this protocol is to address this gap by conducting a national survey using the MCI and MCCI questionnaires.
MATERIALS AND METHODS
Study design
The study adopts a descriptive cross-sectional observational design, based on the administration of a survey aimed at nurses who act as clinical tutors in Nursing Degree Courses in Italy, active in the academic year 2024–2025. The collection of data is scheduled to take place between July and December 2025. The study was registered on the OSF platform [13].
The cross-sectional design was selected to provide a comprehensive overview of the general and cultural competencies of nursing tutors within the context of university education, offering a representative sample of the prevailing circumstances at the time of the survey. This approach aligns with the established guidelines for conducting and reporting observational studies and surveys in the healthcare sector [14,15].
Population and sample
The target population of the study consists of nurses who have provided clinical tutoring for students enrolled in nursing degree programmes during the 2024–2025 academic year. These nurses are employed within healthcare facilities and universities that are affiliated with the training programmes.
Convenience sampling was employed due to the unavailability of a reliable estimate of the total number of nurse tutors at the national level. The use of random sampling was not feasible due to the absence of a national registry or comprehensive database of nurse tutors in Italy, as well as the heterogeneity of the tutor role across institutions.
Participants will be recruited through institutional and professional channels, including nursing degree programme coordinators, internship coordinators, and professional nursing networks, in accordance with the methodology of descriptive surveys in nursing [16].
Given the descriptive and non-probabilistic nature of the study, a formal sample size calculation was not performed. The target sample size of approximately 600 participants was defined pragmatically, with the aim of achieving broad national coverage and ensuring sufficient variability for descriptive and exploratory analyses, as is recommended for descriptive studies not intended for hypothesis testing [15,16].
The study will include nurses who have been employed as clinical tutors during the 2024–2025 academic year. Individuals in managerial roles, those who have been absent for extended periods (e.g., due to illness or pregnancy), and those who have experienced a demotion or downgrade in their professional positions are excluded from this study.
No minimum duration of tutoring experience or formal tutor training was required for inclusion, as the study aims to reflect the heterogeneity of tutoring practices in the Italian clinical education context. Tutor training and professional experience were collected as study variables rather than used as exclusion criteria.
Survey instruments
The questionnaire is composed of three sequential sections.
Preliminary section:
The first section collects socio-demographic and professional information about the respondents. Specifically, the following variables will be collected: age, gender, highest educational qualification, years of professional nursing experience, years of experience in clinical tutoring, current clinical setting, geographical region, prior formal training as a tutor (yes/no), and formal recognition of the tutoring role within the workplace (yes/no). This information will be used to describe the sample and to explore potential associations with mentoring competencies.
Mentors’ Competence Instrument (MCI)
The second section consists of the Mentors’ Competence Instrument (MCI), developed by Tuomikoski et al. [7] and validated in the Italian context [17], based on the 7-factor model. The instrument includes 63 items distributed across seven domains: (1) mentoring practices, (2) mentor characteristics, (3) mentor motivation, (4) goal-oriented mentoring, (5) reflection during mentoring, (6) student-centred evaluation, and (7) constructive feedback and assessment. Items are rated on a 4-point Likert scale ranging from “strongly disagree” to “strongly agree,” with higher scores indicating higher perceived mentoring competence.
The Italian version of the MCI has demonstrated good psychometric properties, with satisfactory construct validity and internal consistency, reporting Cronbach’s alpha values ranging approximately from 0.76 to 0.90 across domains [7].
Mentors’ Cultural Competence Instrument (MCCI):
The third section includes the Mentors’ Cultural Competence Instrument (MCCI), developed and validated by Oikarainen et al. [9], developed to assess cultural competence in mentoring culturally and linguistically diverse nursing students. The instrument comprises 13 items organised into domains addressing cultural awareness, cultural sensitivity, intercultural communication, and linguistic diversity. Responses are measured using a 4-point Likert scale (“strongly disagree” to “strongly agree”), with higher scores reflecting higher perceived cultural competence.
The Italian version of the MCCI has shown acceptable validity and reliability, with evidence of construct validity and good internal consistency across domains.
Bringing MCI and MCCI together will provide a complete and detailed understanding of the tutor's role. This will include both the usual teaching skills and those that are growing in the area of cultural diversity. The original authors of the instruments authorised the researchers to use the instruments on 22/05/2025.
The survey will also comprise three optional open-ended questions with optional answers, inviting respondents to describe the role of the tutor and the strengths and weaknesses of current nursing students.
Data collection procedures
The administration of the questionnaires will be conducted digitally, utilising the Microsoft Form™ platform (Microsoft Corp., Redmond, WA, USA) [18]. The data collection period is scheduled to occur between September and December 2025. The survey will be distributed using a combination of methods: a direct web link disseminated through Nursing Degree Programme Coordinators and internship coordinators at participating universities, and QR codes shared during educational and professional training events targeting nursing tutors.To minimise the risk of unauthorised access or duplicate responses, the survey platform will implement IP address tracking, allowing only one submission per device. Participation will be anonymous, and no personally identifiable information will be collected. A system of periodic reminders will be employed to enhance response rates. Reminder messages will be sent at fortnightly intervals throughout the data collection period. Data confidentiality will be ensured through secure data management procedures. All collected data will be stored exclusively on a password-protected external hard drive, with encrypted access enabled via the BitLocker security system of Windows 10® (Microsoft Corporation, WA, USA). Data will be accessible only to the research team and will be handled in accordance with applicable data protection regulations.
Data analysis
The survey data will be entered into a Microsoft Excel™ 2019 spreadsheet (Microsoft Corp., Redmond, WA, USA) and quality checked by a researcher to ensure accuracy. All responses remain anonymous. The questionnaire will not identify participants. Upon reaching the target sample size or by 31 December 2025, the data will be exported to Excel™ and subsequently analysed using SPSS™ software, version 27 [19]. The statistical analysis will follow a progressive approach: initially, descriptive statistics will be calculated, such as frequencies and percentages for categorical variables, means and standard deviations or medians and interquartile ranges for continuous variables, after verifying the distribution of the data using normality tests.
Inferential analyses will include Chi-square tests to assess associations between categorical variables. For continuous variables, data distribution will be assessed using tests of normality. Parametric tests (t-test or ANOVA) will be applied to normally distributed variables, while non-parametric alternatives (Mann–Whitney U test or Kruskal–Wallis test) will be used when normality assumptions are not met. The level of statistical significance will be set at p < 0.05.
In addition, correlation analyses will be performed to explore associations between continuous competence scores (overall and domain-specific scores of the Mentors’ Competence Instrument and the Mentors’ Cultural Competence Instrument) and selected socio-demographic and professional variables, such as age, years of professional experience, years of tutoring experience, and prior tutor training. Pearson’s correlation coefficient will be used for normally distributed continuous variables, while Spearman’s rank correlation coefficient will be applied for non-normally distributed or ordinal variables. These analyses will be exploratory in nature.
Responses to the open-ended questions will be analysed using a qualitative thematic analysis approach. The analysis will involve familiarisation with the textual data, inductive coding, and the identification of recurring patterns and themes. The qualitative results will be used to complement the quantitative findings and provide a deeper understanding of tutors’ perceptions regarding their role and the strengths and weaknesses of nursing students.
Ethical considerations
The study will be conducted in full compliance with the principles of the Declaration of Helsinki. All participants will receive comprehensive information regarding the objectives and methodologies of the research and will be able to provide their free and informed consent. Participation is voluntary and anonymous: no personal data or information that could directly or indirectly identify participants will be collected. The data shall be used exclusively for scientific research purposes and shall be processed in accordance with Italian law, Legislative Decree No. 196 of 30 June 2003, “Personal Data Protection Code” [20], updated with the new Legislative Decree No. 101/2018 [21], which adapts Italian legislation to the European privacy regulation (EU Reg. No. 679/2016, GDPR) [22]. The study was approved by the Regional Ethics Committee of the Umbria Region on June 18, 2025 (Prot. No. CE-2376/25).
RESULTS
Reporting results
The presentation of results will adhere to the guidelines of the CHERRIES checklist [23], the CROSS Checklist [24] and the recommendations published by Latour and Tume [25], ensuring consistency, transparency and completeness in survey reporting and compliance with international best practices for the description of questionnaire-based studies.
Expected outcomes
The survey is expected to provide a clear and detailed description of the current mentoring and cultural competence profiles of Italian nursing tutors, based on domain-specific scores obtained from validated instruments. The integration of the MCI and MCCI will facilitate the delineation of an overall profile of the clinical tutor, emphasising their strengths and areas for improvement, as well as identifying potential disparities associated with the socio-demographic and professional characteristics of the participants. The data collected will also facilitate the exploratory identification of associations and correlations between mentoring and cultural competence scores and selected socio-demographic and professional variables. The study is expected to provide valuable information to guide institutional strategies for enhancing the role of tutors and improving the quality of clinical training programmes.
LIMITATIONS AND STRENGTHS
Limitations
The study is subject to certain inherent limitations, which are intrinsic to the adopted methodological framework. The utilisation of convenience sampling has the potential to compromise the representativeness of the sample with respect to the national population of nursing tutors. Furthermore, the utilisation of self-assessment tools carries with it the risk of bias, as participants may overestimate or underestimate their own skills. Finally, voluntary participation may favour responses from individuals who are more motivated or sensitive to the topic, thus introducing the possibility of a self-selection bias.
Strengths
A significant strength of the study is the utilisation of two internationally validated instruments, which ensure the reliability and comparability of the data collected. The magnitude of the sample in question serves to enhance the robustness of the statistical analyses conducted, thereby facilitating the identification of potential associations between the various variables under consideration. The content explored is of particular significance for improving mentoring practices and enhancing the role of the clinical tutor, due to its relevance to training needs and organisational policies in the field of nursing education.
CONCLUSION
The present protocol delineates a national survey designed to evaluate the general and cultural competencies of nursing tutors through the MCI and MCCI. The anticipated outcomes of this study are expected to provide valuable insights that will inform the development of training programmes, the enhancement of the quality of clinical placements, and the strengthening of the role of tutors in nursing education.
Author contributions
Conceptualization: G.D.G.
Methodology: G.D.G.; A.G.
Writing—original draft preparation: G.D.G.; Y.L.; S.B.; R.M.
Writing—review and editing: G.D.G.; Y.L.; S.B.;
Supervision: S.B.; Y.L.; R.M.
Ethics statement
The study will be conducted in full compliance with the principles of the Declaration of Helsinki. The study was approved by the Regional Ethics Committee of the Umbria Region on 18/06/2025 (Prot. No. CE-2376/25).
Conflicts of interest
The authors declare no conflicts of interest.
Funding sources
This research received no external funding
Declaration on the use of ai
We used ChatGPT and DeepL.com to assist with English language refinement and grammar checking. No AI was used for interpretation, or scientific content generation.
Data availability statement
Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
Acknowledgment
We would like to thank the Research Unit of Nursing Science and Health Management and the Educational Research Group at the University of Oulu, Finland, for authorising us to use their search tools (MCI and MCCI).
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Exploring the impact of Nurse Manager Leadership Styles on Nurses' Job Performance at Hamad Medical Corporation: A Cross-Sectional Study
Abdelbasset Ghalgaoui 1, 2, *, Mehdi Halleb 1, Maha Mohamed Marzouk Ahmed 1,
Osama Helmi Mohammad Subih 1, Nabil Ajjel 1
- Department of Nursing, Hamad Medical Corporation (HMC), Doha, Qatar.
- Institut Universitaire de Formation des Cadres (INUFOCAD), Port-au-Prince, Haiti.
* Corresponding author: Abdelbasset Ghalgaoui, Graduate Registered Nurse, Department of Nursing, Hamad Medical Corporation (HMC), Doha, Qatar. PhD Student in Education and Governance, Institut Universitaire de Formation des Cadres (INUFOCAD), Port-au-Prince, Haiti. Email: ghalgaouiabdelbasset@gmail.com
Cite this article
ABSTRACT
Introduction: Nurse performance is vital to patient safety and organizational effectiveness. Leadership style is a recognized determinant of performance, influencing consistency, adaptability, and professional growth. Understanding these dynamics is particularly important in multicultural healthcare environments.
Objective: This study explored the impact of nurse manager leadership styles on nurses’ job performance at Hamad Medical Corporation (HMC).
Methods: A cross-sectional survey was conducted with 980 registered nurses recruited through random sampling. Data were collected using a structured questionnaire including socio-demographic characteristics, the Multifactor Leadership Questionnaire (MLQ-5X), and the Nursing Performance Instrument (NPI). Data analysis was performed using SPSS version 26, applying descriptive statistics, and Spearman’s correlation, Mann–Whitney U, and Kruskal–Wallis H tests.
Results: The workforce was predominantly female (72.1%), married (83.7%), and expatriate, with a mean age of 40.4 years. Transactional leadership (2.57 ±085) was the most common style, followed by transformational (2.20±1.05), while passive-avoidant leadership was minimal (1.49±0.97). Transformational leadership demonstrated strong positive associations with consistency of practice and adaptability. Transactional leadership supported compliance but was less effective in stimulating innovation, while passive-avoidant leadership was negatively correlated with performance outcomes.
Conclusion: Transformational leadership emerged as the most effective style for enhancing nurse performance, while transactional leadership sustained compliance without fostering long-term growth. Strengthening transformational leadership among nurse managers at HMC may improve clinical outcomes, adaptability, and organizational performance.
Keywords: Leadership, Nurses, Job Performance, Practice
INTRODUCTION
In the complex and ever-evolving healthcare environment, the performance of nurses is a cornerstone of quality patient care and organizational success [1–4]. Nurses are at the forefront of healthcare delivery, directly influencing patient outcomes, safety, and satisfaction through their clinical skills, critical thinking, and interpersonal interactions [5–7]. The effectiveness with which nurses execute their duties is not solely dependent on their individual competencies but is significantly shaped by the leadership they receive. Nurse managers, in particular, play a crucial role in fostering an environment that optimizes nursing performance, as their leadership styles directly influence the motivation, development, and productivity of their teams [8].
Job performance in nursing encompasses a broad range of behaviors and outcomes, including adherence to protocols, clinical proficiency, teamwork, communication, and adaptability to challenging situations. High-performing nursing teams contribute to reduced medical errors, improved patient recovery rates, and enhanced overall efficiency within healthcare institutions [9–12]. The leadership styles employed by nurse managers have a profound impact on the performance of their nursing staff. Transformational leadership, characterized by its emphasis on inspiring, empowering, and intellectually stimulating nurses, is often associated with higher levels of performance, as it encourages innovation, professional growth, and a strong sense of ownership [13–16]. This style promotes a positive work environment, which is crucial for fostering high performance. In contrast, transactional leadership, which relies on clear directives, rewards, and corrective actions, can ensure compliance with standards but may not always foster the proactive and adaptive behaviors essential for complex clinical environments [17–19]. Passive-avoidant leadership, marked by a lack of engagement and decision-making, typically has detrimental effects on performance, leading to confusion and disorganization[8]. Recent studies continue to highlight the importance of effective nursing leadership in driving performance outcomes [8,14–19].
While existing literature has explored the relationship between nurse manager leadership styles and job performance, there remains a specific research gap concerning the context of Qatar. Studie in Qatar have investigated aspects such as the generational gap between nurses and nurse managers and its potential impact on job performance [20] . However, a comprehensive understanding of how various nurse manager leadership styles directly influence the diverse aspects of nurses' job performance within the unique healthcare landscape of Qatar, considering its multicultural workforce and specific organizational structures, is still limited. There is a particular need to understand which leadership styles are most effective in promoting optimal job performance among nurses in HMC, given the specific cultural and organizational dynamics of the region.
Objective
This study aims to investigate the specific influence of nurse manager leadership styles on nurses’ job performance at HMC. Specifically, it will examine the relationship between transformational, transactional, and laissez-faire leadership styles and various dimensions of nursing performance. The insights gained will be invaluable for developing targeted leadership training programs and organizational policies designed to optimize nursing performance, ultimately contributing to superior patient care and the sustained success of HMC's healthcare mission.
MATERIALS AND METHODS
Type and Classification of Study
This study employed a quantitative, cross-sectional research design to examine the relationship between nurse manager leadership styles and nurses' job satisfaction, work engagement, and job performance at Hamad Medical Corporation (HMC).
Comparisons and Predictors of Interest
The primary focus was on comparing various nurse manager leadership styles and their respective impacts on staff nurses’ job satisfaction, work engagement, and job performance.
Study Duration
The study was conducted over a period of approximately four months, from November 5, 2024, to March 1, 2025.
Sample Size Justification
To ensure reliability and representativeness of the findings, a sample size calculation was conducted based on a population of approximately 12,000 nurses. Using a 95% confidence level and a ±3% margin of error, a sample size of 980 nurses was determined to be appropriate. The sample size was calculated using Cochran’s formula:
where Z = 1.96, p = 0.5, e = 0.03.
The value p=0.5 was selected to provide the most conservative estimate and ensure an adequate sample size in the absence of prior data, while margin of error e = ±3% was chosen to achieve high precision and reliable representativeness of the study results.
Study Population and Setting
The study targeted registered nurses employed across different departments at Hamad Medical Corporation (HMC), Qatar. A simple random sampling procedure was used to select participants. The sampling frame consisted of the complete roster of licensed nurses at HMC, with each nurse assigned a unique identification number. Randomization was performed using Microsoft Excel’s RAND function to generate a randomly ordered list.
To mitigate potential non-response, the initial calculated sample of 980 nurses (based on a 95% confidence level and ±3% margin of error for a population of approximately 12,000 nurses) was increased by 245 nurses, resulting in 1,225 nurses being contacted. The questionnaire was distributed to these nurses via their official HMC email accounts, and 980 responses were received and included in the final study sample. This approach ensured equal probability of selection and broad representation across hospitals and nursing units within HMC.
The study was conducted exclusively within HMC facilities.
Inclusion Criteria
- Registered nurses currently employed at HMC.
- Nurses who voluntarily consented to participate.
- Nurses with a minimum of six months of experience at HMC to ensure familiarity with the organizational culture and leadership practices.
Exclusion Criteria
- Nurses on leave or absent during data collection.
- Nurses in managerial or supervisory roles.
- Contract or temporary nurses.
Data Collection
Data were collected via structured online questionnaires distributed through Google Forms. The survey instruments covered the following areas:
- Socio-demographic Data
- Collected information included age, gender, nationality, years of nursing experience, tenure at HMC, education level, hospital, and department.
- Multifactor Leadership Questionnaire (MLQ-5X)
This 45-item tool assessed leadership styles (transformational, transactional, and laissez-faire) across dimensions such as inspirational motivation, intellectual stimulation, and contingent reward. Responses were recorded on a 5-point Likert scale (0 = "Not at all" to 4 = "Frequently, if not always") [21].
Items were grouped into their respective leadership dimensions using the MLQ scoring key. For each dimension, a mean score was calculated by summing the responses to the items composing that scale and dividing by the number of valid responses. All leadership style subscales consisted of four items each. Blank or missing responses were excluded from the calculations. Higher mean scores indicated more frequent exhibition of the corresponding leadership behaviors. Leadership dimensions were analyzed as continuous variables rather than categorizing leaders into a single leadership style. The tool demonstrated strong reliability, with Cronbach’s alpha ranging from 0.70 to 0.90.
- Nursing Performance Instrument (NPI)
This 20-item instrument assessed nursing performance across clinical and interpersonal dimensions. Responses were rated on a 6-point Likert scale (1 = "Strongly Disagree" to 6 = "Strongly Agree") [22].
NPI scores were calculated by summing the item responses within each domain and dividing by the number of items to obtain mean domain scores. An overall NPI score was computed by averaging all 9 items. Missing responses were excluded from the calculations. Higher scores indicated better perceived nursing performance.
The instrument yielded a Cronbach’s alpha of 0.80, indicating strong reliability.
Statistical Considerations and Data Analysis
Primary and Secondary Outcomes
- Primary Outcomes: Nurses’ job performance.
- Secondary Outcome: The relationship between nurse manager leadership styles and the three primary outcomes.
Data Analysis Plan
1. Descriptive Statistics
Summarized participant characteristics and key variables using means and standard deviations (mean±SD), or medians and interquartile intervals (IQR), for numerical data, while ranges, and percentages for qualitative and categorical data.
2. Inferential Statistics
- Normality of continuous variables was assessed using the Shapiro–Wilk test, which indicated non-normal distribution (p < 0.05).
- Spearman’s Correlation Coefficient: Used to assess associations between leadership styles and outcome variables.
- Mann–Whitney U Test: Applied to compare differences in outcome variables between two independent groups
- Kruskal–Wallis H Test: Used to compare differences across three or more independent groups
- A p-value (p) < 0.05 was considered statistically significant.
Statistical Software
All analyses were performed using SPSS-26 (Statistical Package for the Social Sciences-26).
Ethical Approval and Informed Consent Statement
Informed consent was obtained from all study participants. The purpose, procedures, and voluntary nature of the study were explained through official internal communication channels via HMC e-mail. Participants provided electronic consent after having at least two months to review the study information before deciding to participate. Only registered nurses employed at Hamad Medical Corporation (HMC) who met the inclusion criteria were enrolled. No financial incentives were offered for participation.
The study was approved by the Medical Research Center (MRC) – Local Ethics Committee of Hamad Medical Corporation, Qatar (Protocol No. MRC-01-24-356),with approval granted on 15/08/2024, and was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice (GCP), as well as the regulations of the Ministry of Public Health (MoPH), Qatar. Participant anonymity and data confidentiality were strictly maintained throughout the study.
RESULTS
Demographic and Professional Characteristics
The study sample (N=980) exhibits a predominant representation of females (72.14%), while males account for 27.86%. The sex ratio of 0.39 males per female (Table 1).
| Characteristics | Categories | Frequency (n) | Percent (%) | Mean± SD | Median (IQR) |
| Gender | Male | 273 | 27.86 | ||
| Female | 707 | 72.14 | |||
| Marital Status | Single | 138 | 14.08 | ||
| Married | 820 | 83.67 | |||
| Widowed | 8 | 0.82 | |||
| Separated / Divorced | 14 | 1.43 | |||
| Nationality | Cuban | 36 | 3.67 | ||
| Egyptian | 16 | 1.63 | |||
| Filipino | 332 | 33.88 | |||
| Indian | 413 | 42.14 | |||
| Iranian | 3 | 0.31 | |||
| Jordanian | 64 | 6.53 | |||
| Lebanese | 5 | 0.51 | |||
| Palestinian | 8 | 0.82 | |||
| Somali | 3 | 0.31 | |||
| Sudanese | 51 | 5.20 | |||
| Tunisian | 49 | 5.00 | |||
| Age (years) | ≤30 years | 64 | 6.53 |
40.40 ± 7.89 |
37 (35-46) |
| ]30-45] | 657 | 67.04 | |||
| > 45 | 259 | 26.43 |
Table 1. Demographic Characteristics (N=980)
The majority of participants are married (83.67%), with a smaller proportion being single (14.08%) or widowed (0.82%). In terms of nationality, the most represented groups are Indian (42.14%) and Filipino (33.88%), collectively comprising over 75% of the total sample, while other nationalities, such as Jordanian (6.53%), Sudanese (5.20%), and Tunisian (5.0%), are present in smaller proportions. Certain nationalities, including Iranian (0.31%) and Somali (0.31%), have minimal representation. The mean age of the participants is 40.40 ±7.89 years, with a minimum age of 26 years and a maximum age of 62 years. The majority belonging to the 30-45 age group (67.04%), followed by those over 45 years (26.43%), and only a small percentage ≤30 years (6.53%).
The professional characteristics of the study sample (N=980) reveal a workforce with diverse experience levels and educational backgrounds (Table 2). The mean years of experience as a nurse is 16.85 ± 7.14 years, ranging from 3 to 39 years. The majority have 5-15 years of experience (54.29%), followed by those with more than 15 years (43.57%), and a small proportion with ≤5 years (2.14%). Experience within Hamad Medical Corporation (HMC) follows a similar trend, with a mean of 9.93 ± 7.54 years, ranging from 1 to 36 years. The distribution shows that 36.73% have ≤5 years, 37.65% have 5-15 years, and 25.61% have over 15 years of experience in HMC.
Regarding education, the majority hold a Bachelor’s degree (76.63%), while 14.18% have a diploma, and 9.18% hold a Master’s degree or higher.
The participants are distributed across various hospitals, with the highest representation from Hamad General Hospital (27.55%), followed by Rumailah Hospital (11.63%), Al Wakra Hospital (11.43%), and Women’s Wellness and Research Center (8.16%). Other facilities, including specialty hospitals like the Communicable Disease Center (1.53%) and The Cuban Hospital (1.73%), have lower representation.
In terms of departmental distribution, the Surgical Department (35.51%) and Medical Department (30.20%) have the highest number of participants, followed by Critical Care/Emergency Services (22.45%) and Outpatient and Ambulatory Services (11.84%).
| Characteristics | Categories | Frequency (n) | Percent (%) | Mean± SD | Median (IQR) |
| Years of experience as a nurse | ≤5 years | 21 | 2.14 |
16.85 ± 7.14 |
15(12-22) |
| ]5-15] | 532 | 54.29 | |||
| > 15 | 427 | 43.57 | |||
| Years of experience in HMC | ≤5 years | 360 | 36.73 |
9.93 ± 7.54 |
7(4-17) |
| ]5-15] | 369 | 37.65 | |||
| > 15 | 251 | 25.61 | |||
| Educational background
|
Diploma | 139 | 14.18 | ||
| Bachelor’s degree | 751 | 76.63 | |||
| Master’s degree | 90 | 9.18 | |||
| Hospital
|
Hamad General Hospital | 270 | 27.55 | ||
| Ambulatory Care Center | 58 | 5.92 | |||
| Qatar Rehabilitation Institute | 17 | 1.73 | |||
| NCCCR | 19 | 1.94 | |||
| Mental Health Service | 48 | 4.90 | |||
| Communicable Disease Center | 15 | 1.53 | |||
| Al Khor Hospital | 72 | 7.35 | |||
| Rumailah Hospital | 114 | 11.63 | |||
| Al Wakra Hospital | 112 | 11.43 | |||
| Hazm Mebaireek General Hospital | 64 | 6.53 | |||
| Aisha Bint Hamad Al Attiyah Hospital | 63 | 6.43 | |||
| The Cuban Hospital | 17 | 1.73 | |||
| Women's Wellness and Research Center | 80 | 8.16 | |||
| Heart Hospital | 31 | 3.16 | |||
| Department
|
Critical Care / Emergency Services | 220 | 22.45 | ||
| Medical Department | 296 | 30.20 | |||
| Surgical Department | 348 | 35.51 | |||
| Outpatient (OPD) and Ambulatory Services | 116 | 11.84 |
Table 2. Professional Characteristics (N=980)
Nurse Manager Leadership Styles
The results indicate that transactional leadership (2.57±0.85) is more dominant than transformational leadership (2.20±1.05), suggesting that leaders in this sample primarily rely on structured management approaches, such as performance-based rewards (contingent reward, 2.56±1.05) and active monitoring (management by exception – active: 2.58±0.98), rather than fostering innovation, motivation, or individualized consideration (Table 3).
| Minimum | Maximum | Mean | S D | Median | Q1 | Q3 | |
| Idealized Attributes or Idealized Influence (Attributes) | 0.00 | 4.00 | 2.19 | 1.14 | 2.25 | 1.50 | 3.00 |
| Idealized Behaviors or Idealized Influence (Behaviors) | 0.00 | 4.00 | 2.35 | 1.15 | 2.50 | 1.75 | 3.25 |
| Inspirational Motivation | 0.00 | 4.00 | 2.34 | 1.22 | 2.50 | 1.50 | 3.25 |
| Intellectual Stimulation | 0.00 | 4.00 | 2.21 | 1.11 | 2.25 | 1.50 | 3.00 |
| Individual Consideration | 0.00 | 4.00 | 1.94 | 0.96 | 2.00 | 1.25 | 2.75 |
| Transformational | 0.00 | 4.00 | 2.20 | 1.05 | 2.35 | 1.55 | 3.00 |
| Contingent Reward | 0.00 | 4.00 | 2.56 | 1.05 | 2.75 | 2.00 | 3.25 |
| Mgmt by Exception (Active) | 0.00 | 4.00 | 2.58 | 0.98 | 2.75 | 2.00 | 3.25 |
| Transactional | 0.25 | 4.00 | 2.57 | 0.85 | 2.62 | 2.00 | 3.12 |
| Mgmt by Exception (Passive) | 0.00 | 4.00 | 1.55 | 1.01 | 1.25 | 0.75 | 2.25 |
| Laissez-Faire | 0.00 | 4.00 | 1.43 | 1.05 | 1.25 | 0.50 | 2.25 |
| Passive Avoidant | 0.00 | 4.00 | 1.49 | 0.97 | 1.37 | 0.75 | 2.12 |
| Extra Effort | 0.00 | 4.00 | 2.17 | 1.20 | 2.33 | 1.00 | 3.00 |
| Effectiveness | 0.00 | 4.00 | 2.25 | 1.22 | 2.50 | 1.00 | 3.00 |
| Satisfaction | 0.00 | 4.00 | 2.28 | 1.31 | 2.50 | 1.00 | 3.00 |
| Outcomes of Leadership | 0.00 | 400 | 2.23 | 1.20 | 2.42 | 1.05 | 3.16 |
Table 3. Nurse Manager Leadership Styles
Within transformational leadership, the highest subscale is idealized influence behaviors (2.35±1.15), indicating that some leaders demonstrate strong role-modeling behaviors. However, individual consideration (1.94±0.96) is the lowest, suggesting that leaders may not provide enough mentorship or personalized support to the nurses.
The passive-avoidant leadership style (1.49±0.97) has the lowest overall scores, particularly laissez-faire leadership (1.43±1.05), indicating that leaders in this sample are generally engaged and do not frequently avoid decision-making. However, the management by exception – passive score (1.55±1.01) suggests that some leaders may still wait until problems arise before taking corrective action.
Regarding leadership outcomes, the scores for effectiveness (2.25±1.22) and satisfaction (2.28±1.31) indicate moderate levels of perceived leader effectiveness and staff satisfaction. Overall outcomes of leadership (2.23±1.20) reflect a tendency towards average performance across the sample, with some variability.
Nurses' Job Performance
The results of the Nursing Performance Instrument (NPI) and its three subscales reveal interesting insights into the nursing workforce’s performance (Table 4).
| Minimum | Maximum | Mean | SD | Median | Q1 | Q3 | |
| Physical / mental decrement | 1.00 | 6.00 | 2.91 | 1.11 | 3.00 | 2.00 | 3.66 |
| Consistent practice | 1.00 | 6.00 | 4.73 | 1.29 | 5.00 | 4.25 | 5.75 |
| Behavioral change | 1.00 | 6.00 | 3.61 | 1.33 | 3.50 | 3.00 | 4.50 |
| Nursing Performance Instrument (NPI) | 1.00 | 5.78 | 3.88 | 0.94 | 3.88 | 3.44 | 4.44 |
Table 4. Nurses' Job Performance
The subscale "Physical/Mental Decrement" had a mean score of 2.91±1.11, suggesting that nurses report a moderate level of physical and mental strain, though it is not perceived as a severe issue overall. The "Consistent Practice" subscale scored the highest, with a mean of 4.73±1.29, indicating that nurses generally perceive themselves as maintaining consistent and stable practices in their roles. The "Behavioral Change" subscale, with a mean of 3.61±1.33, suggests that there is moderate evidence of behavioral changes in nursing practice. Lastly, the overall NPI score of 3.88±0.94 indicates a generally positive view of nursing performance, reflecting a moderate level overall.
Comparison of Socio-demographic Characteristics and Their Association with Nurses' Job Performance
Female nurses had significantly higher job performance than male nurses (mean rank: 536.67 vs. 370.92, p < 0.001) (Table 5).
| Characteristics | Categories | Mean Rank | Test | p-value (test) |
| Gender
|
Male | 370,92 | 63861,5 | < 0.001 (MW)*
|
| Female
|
536,67 | |||
| Marital Status | Single | 551.12 |
9.513
|
0.009 (KW)*
|
| Married | 472.37 | |||
| Widowed | 457.50 | |||
| Nationality | Cuban | 268.22 | 150.584
|
< 0.001 (KW)*
|
| Egyptian | 389.00 | |||
| Filipino | 576.75 | |||
| Indian | 498.81 | |||
| Iranian | 715.83 | |||
| Jordanian | 298.00 | |||
| Lebanese | 717.70 | |||
| Palestinian | 754.00 | |||
| Somali | 649.17 | |||
| Sudanese | 486.15 | |||
| Tunisian | 198.83 | |||
| Age (years) | ≤30 years | 568.97 | 29.617
|
< 0.001 (KW)*
|
| ]30-45] | 456.10 | |||
| > 45 | 558.38 | |||
| Years of experience as a nurse
|
≤5 years | 509.26 | 1.273
|
0.529 (KW)*
|
| ]5-15] | 481.21 | |||
| > 15 | 501.15 | |||
| Years of experience in HMC
|
≤5 years | 472.97 | 0.003 | 11.533 (KW)* |
| ]5-15] | 472.07 | |||
| > 15 | 542.74 | |||
| Educational background | Diploma | 425.49 | 9.391
|
0.009 (KW)*
|
| Bachelor’s degree | 498.19 | |||
| Master’s degree | 526.72 | |||
| Hospital | Hamad General Hospital | 527.88 | 61.003 | < 0.001(KW)* |
| Al Khor Hospital | 375.75 | |||
| Rumailah Hospital | 428.29 | |||
| Al Wakra Hospital | 458.57 | |||
| Hazm Mebaireek General Hospital | 500.56 | |||
| Aisha Bint Hamad Al Attiyah Hospital | 462.40 | |||
| The Cuban Hospital | 263.56 | |||
| Women's Wellness and Research Center | 481.40 | |||
| Heart Hospital | 607.08 | |||
| Ambulatory Care Center | 531.40 | |||
| Qatar Rehabilitation Institute | 583.32 | |||
| NCCCR | 683.03 | |||
| Mental Health Service | 493.17 | |||
| Communicable Disease Center | 703.83 | |||
| Department | Critical Care / Emergency Services | 492.86 | 43.713 | < 0.001(KW)* |
| Medical Department | 488.47 | |||
| Surgical Department | 440.61 | |||
| Outpatient (OPD) and Ambulatory Services | 640.84 |
Note: MW = Mann–Whitney U test; KW = Kruskal–Wallis H test; *p < 0.05 indicates statistical significance.
Table 5. Comparison of Socio-demographic Characteristics and Their Association with Nurses' Job Performance.
Single nurses reported the highest performance, followed by married and widowed nurses (551.12 vs. 472.37 vs. 457.50, p = 0.009). Significant differences were observed across nationalities, with Palestinian nurses showing the highest performance and Tunisian nurses the lowest (754.00 vs. 198.83, p < 0.001).
Regarding age, nurses aged ≤30 years had the highest performance, followed by those >45 years and those aged 30–45 years (568.97 vs. 558.38 vs. 456.10, p < 0.001). Years of experience as a nurse were not significantly associated with performance, although nurses with ≤5 years of experience had higher performance than those with >15 years or 5–15 years (509.26 vs. 501.15 vs. 481.21, p = 0.529).
Years of experience at HMC were significantly associated with performance, with nurses having >15 years of experience showing the highest performance and those with 5–15 years the lowest (542.74 vs. 472.07, p = 0.003). Educational background influenced performance, with nurses holding a Master’s degree reporting the highest and those with a diploma the lowest (526.72 vs. 425.49, p = 0.009).
Job performance differed significantly across hospitals, with the Mental Health Service reporting the highest and ABAH the lowest performance (703.83 vs. 263.56, p < 0.001). Finally, departmental differences were significant, with the Surgical Department showing the highest performance and the Medical Department the lowest (640.84 vs. 440.61, p < 0.001).
Correlation between Nurse Manager Leadership Styles and Nurses’ Performance
Table 6 explores the relationships between leadership styles and various aspects of nursing performance, including physical/mental decrement, consistent practice, behavioral change, and overall performance measured by the Nursing Performance Instrument (NPI).
Transformational leadership shows a moderate positive correlation with consistent practice (rho = 0.323, p <0.001) and a weak positive correlation with nursing performance (rho = 0.146, p < 0.001). However, there are no significant relationships with physical/mental decrement (rho = 0.017, p = 0.597) or behavioral change (rho = 0.022, p = 0.489). These results suggest that transformational leadership encourages consistent practice and slightly enhances overall performance but does not appear to directly influence nurses' physical or mental well-being or their immediate behavioral adjustments.
|
|
Physical/mental decrement | Consistent practice | Behavioral change | Nursing Performance Instrument (NPI) | |
| Transformational
|
Spearman Coefficient | 0.017 | 0.323 | 0.022 | 0.146 |
| p-value | 0.597 | < 0.001 | 0.489 | <0.001 | |
| Transactional
|
Spearman Coefficient | -0.277 | -0.055 | -0.339 | -0.230 |
| p-value | < 0.001 | 0.083 | < 0.001 | < 0.001 | |
| Passive Avoidant
|
Spearman Coefficient | 0.038 | -0.073 | -0.087 | -0.121 |
| p-value | 0.233 | 0.022 | 0.006 | < 0.001 | |
Table 6. Correlation between Nurse Manager Leadership Styles and Nurses’ Performance.
Transactional leadership presents a negative correlation with physical/mental decrement (rho = -0.277, p < 0.001), behavioral change (rho = -0.339, p < 0.001), and nursing performance (rho = -0.230, p < 0.001). The correlation with consistent practice is not significant (rho = -0.055, p = 0.083). These findings imply that transactional leadership may be associated with declines in behavioral adaptability and overall performance, potentially reflecting a rigid, reward-punishment dynamic that does not foster flexibility or proactive nursing behaviors.
Passive-avoidant leadership demonstrates weak negative correlations with consistent practice (rho = -0.073, p = 0.022), behavioral change (rho = -0.087, p = 0.006), and nursing performance (rho = -0.121, p < 0.001), though no significant relationship is found with physical/mental decrement (rho = 0.038, p = 0.233). This suggests that passive-avoidant leadership slightly undermines effective nursing practices and performance, likely due to a lack of guidance and support.
In summary, transformational leadership has the most positive influence, especially on consistent practice and overall nursing performance. In contrast, transactional leadership seems linked to negative outcomes, particularly regarding behavioral flexibility and performance, while passive-avoidant leadership also has small but significant negative effects.
DISCUSSION
Demographic and Professional Characteristics
The demographic characteristics of the sample provide important context for interpreting job performance outcomes. The high representation of women (72.14%) is consistent with the global nursing workforce [23,24], though the smaller proportion of men (27.86%) may affect team diversity and performance styles [25]. The predominance of married nurses (83.67%) suggests stability, yet also underscores the dual stressors of family and professional responsibilities, which can affect concentration and efficiency [26,27]. The reliance on expatriate staff, especially Indian (42.14%) and Filipino (33.88%) nurses, reflects regional workforce trends but introduces cultural adaptation challenges that may shape performance consistency [28]. The average age (40.40 years) and extensive experience (16.85±7.14 years) demonstrate a mature workforce capable of sustaining performance. However, the limited presence of younger nurses (≤5 years’ experience, 2.14%) may hinder succession planning and innovation. The predominance of bachelor’s degrees (76.63%) indicates solid educational preparation, though the limited advanced degrees (9.18%) highlight opportunities to strengthen specialized competencies.
Nurse Manager Leadership Styles
Leadership findings confirmed transactional leadership (2.57±085) as the dominant style, with contingent rewards (2.56±1.05) and active monitoring (2.58±0.98) driving structured compliance. While these strategies ensure adherence to standards, they may not stimulate the innovation and adaptability increasingly demanded in modern healthcare settings [15,18]. The low emphasis on individual consideration (1.94±0.96) suggests a lack of personalized development, limiting opportunities for performance growth [8]. By contrast, transformational leadership has been consistently linked to enhanced job performance across diverse contexts [14,16]. Although passive-avoidant leadership (1.49±0.97) was rare, its occasional presence risks undermining performance through delayed intervention. These results suggest that adopting transformational leadership at HMC could strengthen consistency, adaptability, and clinical performance.
Nurses' Job Performance
The high consistent practice scores (4.73±1.29) highlight nurses’ reliability in adhering to established protocols, a strength in error-prone healthcare settings. However, moderate behavioral change (3.61±1.33) signals resistance to adapting workflows, possibly due to rigid transactional leadership or fear of reprisal for deviations. The overall performance score (NPI = 3.88) suggests competence but not excellence, aligning with environments prioritizing compliance over innovation. Notably, physical/mental decrement (2.91±1.11) indicates that strain, while not severe, may hinder proactive initiatives. In a similar context in Iran, nurse performance was also reported at a moderate level, with the general performance aspect receiving the highest average score and the mental aspect the lowest [29].
Comparison of Socio-demographic Characteristics and Their Association with Nurses' Job Performance
Job performance varied markedly across demographics. Females outperformed males (p < 0.001), aligning with a study conducted in the same context in Jordan, a Middle Eastern country, which linked female nurses to higher job performance [30]. This gender gap may reflect both enduring social norms around caring roles and targeted soft-skills training that disproportionately benefits female practitioners.
Single nurses showed higher performance (mean rank = 551.12) than married or widowed peers, possibly due to fewer familial responsibilities or greater focus on career progression. This contrasts with studies conducted in Jordan and Turkey, which found no significant relationship between marital status and job performance [30,31]. Nationality-based differences were stark: Palestinian nurses (mean rank = 754.00) excelled, while Tunisians (mean rank = 198.83) underperformed. This may reflect disparities in training quality, language proficiency, or workplace integration. Younger nurses (≤30 years) outperformed older colleagues (p < 0.001), suggesting adaptability to new protocols or technologies. Paradoxically, nurses with >15 years of HMC experience also performed well, indicating that institutional knowledge complements innovation. In the same context, a study conducted in Jordan found that age and experience were related to job performance [30].
Master’s-trained nurses (mean rank = 526.72) outperformed diploma holders, underscoring the value of advanced education in clinical decision-making. Hospitals like the Mental Health Service (mean rank = 703.83). Surgical departments (mean rank = 640.84) reported superior performance, likely due to specialized workflows or interdisciplinary collaboration. These findings advocate for competency-based training and equitable recognition of diverse backgrounds.
Correlation between Nurse Manager Leadership Styles and Nurses’ Job Performance
Transformational leadership moderately enhanced consistent practice (rho = 0.323, p < 0.001) but had no impact on behavioral change, suggesting it fosters reliability over innovation. The findings partially align with those reported by Mohammed Qtait on 2023, who conducted a systematic review of 12 quantitative studies investigating the relationship between leadership styles and nurse performance, reports that transformational leadership had the strongest positive correlation enhancing nursing care quality, job satisfaction, motivation, and patient outcomes [8].
Transactional leadership correlated negatively with performance (rho = -0.230, p < 0.001), particularly behavioral change (rho = -0.339, p < 0.001), implying rigid reward-punishment systems hinder adaptability. However, Qtait’s review found a moderate positive correlation between transactional leadership and nurse performance, indicating some benefits under structured systems [8].
Passive-avoidant leadership also undermined performance (rho = -0.121, p < 0.001), in line with Qtait’s conclusion that laissez-faire leadership had weak or no positive impact [8]. This consistent finding emphasizes that ambiguity, lack of guidance, and disengagement by leaders can significantly reduce nurse motivation and clarity in roles.
Recommendations
The findings of this study highlight the critical need to strengthen transformational leadership competencies among nurse managers at Hamad Medical Corporation (HMC). It is recommended that HMC invest in ongoing leadership development programs that emphasize communication, motivation, and professional empowerment to promote inspiring and participative managerial behaviors. Transformational leadership, by encouraging autonomy and creativity, can significantly enhance both individual and collective nursing performance, fostering consistency in clinical practice and adaptability in complex healthcare settings.
Furthermore, leadership competency assessments should be integrated into managerial performance evaluations to ensure that adopted leadership styles align with organizational goals and contribute to nurse productivity and job satisfaction. Organizational culture should also move toward reducing overreliance on transactional leadership, which focuses primarily on control and rewards, and instead foster more collaborative, innovative, and supportive leadership approaches.
Finally, creating a psychologically and physically supportive work environment is essential to reduce stress and fatigue among nurses, both of which can negatively affect long-term job performance
Strengths and limitations of the study
This study possesses several methodological strengths that enhance its scientific credibility. First, the use of a large and randomly selected sample (N = 980) provides strong representativeness and statistical reliability. The application of validated international instruments, namely the Multifactor Leadership Questionnaire (MLQ-5X) for assessing leadership styles and the Nursing Performance Instrument (NPI) for measuring clinical performance, adds to the study’s methodological rigor. Moreover, the use of robust statistical analyses including Spearman’s correlation, Mann–Whitney U, and Kruskal–Wallis H tests enabled comprehensive exploration of relationships between leadership styles and various aspects of job performance, providing a multidimensional understanding of these dynamics. Despite its strengths, the study also presents certain limitations. The most significant is its cross-sectional design, which limits the ability to infer causality between leadership style and nurse performance. It remains unclear whether transformational leadership directly improves performance, or whether nurses who perform better perceive their leaders as more transformational. Additionally, self-reported data may have introduced response bias, as participants could overestimate their performance due to social desirability or professional pride. Studies would provide a broader and more causal understanding of these leadership–performance relationships.
CONCLUSION
This study clearly demonstrates that nurse manager leadership styles have a significant and differentiated impact on nurses’ job performance within Hamad Medical Corporation. The results reveal that transformational leadership exerts the most substantial positive effect, enhancing consistency in clinical practice, adaptability to change, and overall professional performance. Nurses who perceive their leaders as visionary, supportive, and encouraging are more motivated, committed, and productive. These findings align with international literature showing that transformational leaders foster collaboration, reduce clinical errors, and improve both patient outcomes and staff well-being. In contrast, transactional leadership, while effective in maintaining compliance and operational discipline, tends to have limited influence on creativity and long-term professional growth. Its focus on control and reward systems may sustain performance in routine tasks but fails to nurture the initiative and innovation required in dynamic healthcare environments. On the other hand, passive-avoidant leadership emerges as the least effective style, being associated with disorganization, lack of motivation, and decreased performance due to minimal managerial involvement or guidance.
The implications for nursing leadership are profound. Developing a structured and culturally adaptive transformational leadership model should be a strategic priority for HMC. Such an approach can strengthen clinical performance, enhance innovation, reduce turnover, and promote a collaborative culture focused on quality and patient safety. Ultimately, this study underscores that effective leadership in nursing transcends task management it is fundamentally about mobilizing human potential to achieve excellence, empowerment, and resilience within healthcare organizations.
Local Ethics Committee approval
The study was approved by the Medical Research Center (MRC) – Local Ethics Committee of Hamad Medical Corporation, Qatar (Protocol No. MRC-01-24-356) and was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice (GCP), as well as the regulations of the Ministry of Public Health (MoPH), Qatar. Participant anonymity and data confidentiality were strictly maintained throughout the study.
Conflicts of interest
This study was conducted in accordance with ethical standards. All participants provided informed consent. The authors declare no conflict of interest.
Sources of funding
This research received funding from the Medical Research Center at HMC.The authors thank the
Author contributions
Conception and design: Abdelbasset Ghalgaoui
Data collection: Abdelbasset Ghalgaoui
Data analysis and interpretation: Abdelbasset Ghalgaoui, Osama Helmi Mohammad Subih, Maha Mohamed Marzouk Ahmed, Mehdi Halleb, Nabil Ajjel.
Drafting of the manuscript: all authors.
Critical revision of the manuscript: Abdelbasset Ghalgaoui, Mehdi Halleb
Final approval: all authors
Acknowledgements
The authors thank the staff of Hamad Medical Corporation for their collaboration.
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