Learning Strategies as Correlates of Clinical Skills among Nursing Students in North-West Nigeria

Yakubu Lawali ^1,*, Muhammad Musa Mayana ², Awwalu Shuaibu ³, Abdulrahman Salihu Kombo ⁴, Musa Zubairu ¹

1. Department of Nursing Sciences, Federal University Gusau, Gusau, Zamfara State, Nigeria.
2. Federal Medical Centre, Gusau, Zamfara State, Nigeria.
3. College of Nursing Sciences, Gusau, Zamfara State, Nigeria.
4. Department of Nursing Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.

* Corresponding author: Associate Professor Yakubu Lawali, Department of Nursing Sciences, Federal University Gusau, Zamfara State, Nigeria – E-mail: dryakubulawali@gmail.com

Cite this article

ABSTRACT

Background: Competency-based nursing education requires students to demonstrate observable clinical skills, yet performance may vary according to how students organise, monitor and regulate their learning. Evidence linking multidimensional learning strategies with directly assessed clinical skills remains limited in Nigerian nursing education.

Objective: To determine the relationship between learning strategies and clinical skills and to compare learning-strategy and clinical-skills scores between experimental and control groups of nursing students in North-West Nigeria.

Methods: Quantitative quasi-experimental pre-test–post-test non-equivalent control-group study was conducted in two public nursing schools. Of 190 Year Two students, 152 agreed to participate and 128 were selected systematically: 64 in the experimental group and 64 in the control group. The experimental group received six weeks of structured academic coaching, while the control group continued routine instruction. Learning strategies were measured with the 64-item Learning and Study Strategies Inventory, and clinical skills were assessed using standardised checklists for taking vital signs, care of pressure areas, maintenance of oral hygiene and preparation of an unoccupied bed. Group comparisons, Pearson correlation, sensitivity analysis and hierarchical multiple linear regression were conducted.

Results: At baseline, the groups did not differ significantly in overall learning strategies (experimental: 44.2 ± 13.0; control: 46.4 ± 12.5; p = 0.331) or overall clinical skills (43.9 ± 6.5 versus 45.0 ± 4.8; p = 0.278). At post-test, the experimental group had higher learning-strategy scores (72.0 ± 8.0 versus 53.8 ± 12.9; p < 0.001) and clinical-skills scores (78.4 ± 6.7 versus 59.3 ± 7.5; p < 0.001). Learning strategies were moderately correlated with clinical skills, r(126) = 0.556, 95% CI 0.423–0.665, p < 0.001, with a comparable Spearman coefficient (rho = 0.548, p < 0.001). In the adjusted model, learning strategies remained independently associated with clinical skills (B = 0.23, β = 0.27, p < 0.001). No substantial ceiling effect was observed for the overall outcomes.

Conclusion: Stronger learning strategies were independently associated with higher observed clinical-skills scores after adjustment for group allocation, baseline scores, age and sex. Strategy-focused educational support may be beneficial when integrated with supervised clinical practice; however, the non-randomised school-level allocation and residual institutional confounding require cautious interpretation.

Keywords: clinical skills; learning strategies; nursing education; nursing students; self-regulated learning; quasi-experimental study; Nigeria

INTRODUCTION

Competency-based nursing education focuses on students’ demonstrated ability to integrate knowledge, psychomotor performance, communication, professional judgement and ethical conduct in practice. Contemporary competency frameworks emphasise progressive demonstration, assessment, feedback and reflection rather than reliance on course completion alone [1,2]. Clinical competence is therefore broader than clinical skills: competence represents the integrated capacity to practise safely and appropriately, whereas clinical skills are observable procedural performances that form one component of that wider capacity.

Learning strategies refer to the deliberate cognitive, behavioural and motivational processes used to acquire, organise, retain and apply information. The Learning and Study Strategies Inventory conceptualises strategic learning through skill, will and self-regulation. Skill concerns information processing, selection of main ideas and test strategies; will concerns attitude, motivation and management of learning-related anxiety; and self-regulation concerns concentration, time management, self-testing and use of academic resources [3]. Learning strategies are related to, but distinct from, self-regulated learning. Specific strategies are the techniques students use, while self-regulated learning is the broader cyclical process through which learners set goals, select strategies, monitor progress, interpret feedback and modify their behaviour [4,5].

This distinction is relevant to nursing education because clinical procedures require students to translate theoretical knowledge into ordered, context-sensitive actions. Before performing a procedure, the student must identify the objective, retrieve relevant principles and plan the sequence of care. During performance, the student must sustain attention, monitor accuracy and respond appropriately to the patient. Afterward, the student must evaluate the outcome and use feedback to guide further practice. Learning strategies may therefore influence how effectively clinical exposure is converted into demonstrated skill.

Empirical evidence generally supports the relevance of self-regulation to nursing students’ clinical learning, although the measures and outcomes vary across studies. Kurt and Eskimez [6] reported that self-regulated learning was relevant to adaptation in clinical practice, while Kim [7] identified self-regulated learning as a pathway linking emotional intelligence and collaboration with clinical performance. Subaş and Karacay [8] and Tanimura et al. [9] further demonstrated that nursing students differ in their strategic regulation of clinical learning. Intervention evidence also indicates that structured reflection, assessment and critical-thinking activities can strengthen self-regulation and clinical performance [10]. However, studies have frequently relied on self-reported competence, academic grades or constructs such as learning styles that are not equivalent to learning strategies [11].

Instructional support may be especially important when clinical learning opportunities are limited or uneven. Evidence from accelerated and undergraduate nursing programmes suggests that explicit support for planning, monitoring, feedback use and time management can facilitate self-regulated learning [12,13]. Nevertheless, evidence directly relating multidimensional learning-strategy scores to objectively observed clinical procedures remains comparatively limited in Nigerian nursing education. This gap is important because clinical-skills assessments provide a more direct indication of students’ ability to perform care than self-perceived competence alone.

Nigerian evidence supports the relevance of the educational context. Anarado et al. [16] identified inadequate equipment, limited teacher participation in clinical supervision, incomplete preparation before placements and lack of shift-based preceptors as barriers to nursing students’ clinical training. Akinpelu et al. [17] similarly reported that Nigerian clinical students perceived weaknesses in infrastructure, communication and lecturer–student relationships within their learning environment. More recent Nigerian evidence shows that clinical competence varies among nursing students and may be associated with individual characteristics [18]. These findings indicate that clinical learning outcomes arise from interacting student, instructional and institutional factors, while evidence specifically linking multidimensional learning strategies with directly observed procedures remains limited.

The present study examined Year Two students in two public nursing schools in North-West Nigeria. It addressed the evidence gap by assessing learning strategies and four directly observed clinical procedures in experimental and control groups before and after a structured educational intervention. The study objective was to determine the relationship between learning strategies and clinical skills and to compare the two variables between groups. The null hypothesis stated that there was no statistically significant relationship between learning strategies and clinical skills.

The conceptual relationship between students’ learning strategies and their observed clinical skills performance is illustrated in Figure 1. The framework demonstrates how key dimensions of learning strategies, including skill, will, and self-regulation, may influence the development and demonstration of clinical competencies.

The group comparison describes score distributions before and after the intervention; the principal correlational pathway concerns the relationship between learning strategies and observed clinical skills.

Figure 1. Conceptual framework linking learning strategies with clinical skills.

METHODS

Study design

A quantitative quasi-experimental pre-test–post-test non-equivalent control-group design was used. School of Nursing Gusau, Zamfara State, constituted the experimental school, while School of Nursing Sokoto constituted the control school. The schools were assigned by site rather than by individual randomisation; therefore, the design permitted group comparison while retaining the possibility of baseline differences between institutions. This study was guided by a conceptual framework that considers learning strategies as important determinants of clinical skills acquisition among nursing students. The framework incorporates three major learning strategy components: skill (knowledge of how to learn and perform tasks), will (motivation, effort, and confidence), and self-regulation (planning, monitoring, and evaluation). These components are proposed to contribute to students’ clinical skills performance, as assessed through selected nursing procedures (Figure 1).

Setting, population and participants

The study was conducted in two public nursing schools in Zamfara and Sokoto States, North-West Nigeria. The target population comprised 190 Year Two nursing students: 85 in Gusau and 105 in Sokoto. Year Two students were selected because they had received foundational clinical instruction and were available to participate throughout the intervention and follow-up periods. All 190 Year Two students in the two selected schools were approached during the information sessions. Of these, 152 agreed to participate and entered the sampling frame, while 38 did not proceed to consent. Systematic sampling was then used within each school to select 128 students, with 64 allocated by school to the experimental group and 64 to the control group. All 128 participants completed both pre-test and post-test assessments and were included in the final analysis (Figure 2). The remaining 24 consenting students were not selected because the predetermined sample size had been reached. All 128 selected participants completed the pre-test and post-test assessments and were included in the analysis.

Sample-size determination

The sample size was calculated for comparison of two independent means [14]. Using a two-sided 5% significance level (Zα/2 = 1.96), 80% power (Zβ = 0.84), an assumed standard deviation of 20 and a minimum detectable mean difference of 10, the required number was calculated as

participants per group. This was rounded to 64 per group, producing a total sample of 128 students.

The selection and progression of participants through the study are presented in Figure 2. The figure illustrates the movement of participants from the target population through consent, sampling, group allocation, completion of pre-test and post-test assessments, and inclusion in the final analysis.

Figure 2. Flow diagram showing the recruitment, selection, allocation, follow-up, and final inclusion of Year Two nursing students in the experimental and control groups..

Intervention and control conditions

Four qualified clinical instructors from the experimental school completed a three-day preparation programme on the coaching protocol, constructive feedback, student collaboration, communication and progress monitoring. The 64 experimental-group students were divided into four groups of 16 and assigned to the trained instructors. The intervention lasted six weeks. Sessions were conducted weekly and generally lasted 60–90 minutes.

The intervention combined goal setting, identification of learning barriers, action planning, explicit learning-strategy instruction, demonstration and supervised practice of the selected procedures, immediate feedback, self-evaluation and follow-up. Progress was reviewed through a four-step sequence of review, evaluation, anticipation of barriers and planning [15]. Students were expected to undertake peer practice and clinical application between sessions. The control group continued the routine educational and clinical programme provided by its school. The post-test was conducted within two months after the final coaching session using the same learning-strategy and clinical-skills measures administered at baseline.

Measurement of learning strategies

Learning strategies were assessed using the 64-item Learning and Study Strategies Inventory [3]. Items were rated on a five-point scale from 1 (not at all typical of me) to 5 (very much typical of me) and scored according to the instrument key, including reverse scoring where applicable. The ten scales were grouped into skill (information processing, selecting main ideas and test strategies), will (attitude, motivation and anxiety management) and self-regulation (time management, concentration, self-testing and use of academic resources). The English-language version was retained because English was the medium of instruction. Nursing-education, clinical-nursing, educational-measurement and language experts reviewed the instrument for clarity and cultural relevance in North-West Nigeria. Minor contextual wording changes were made without altering the construct structure, response options or scoring, and the instrument was pilot-tested among 20 Year Two nursing students in a comparable public nursing school outside the study sites. Percentage scores were calculated as the obtained raw score divided by the maximum obtainable raw score and multiplied by 100. Continuous scores were retained for analysis to avoid unsupported categorical cut-offs.

Published scale-level Cronbach’s alpha coefficients ranged from 0.68 to 0.89, with test–retest coefficients of 0.72–0.85. Preliminary testing in the study yielded test–retest and split-half coefficients of 0.87 and 0.70, respectively. Using pre-intervention responses from all 128 participants, Cronbach’s alpha was 0.89 for skill, 0.87 for will and 0.90 for self-regulation; the ten scale-level coefficients ranged from 0.74 to 0.86, and the complete instrument coefficient was 0.93, indicating acceptable to good internal consistency across the scales and high internal consistency for the broader scores.

A published psychometric validation study of this specific 64-item version among Nigerian nursing students was not identified. Contextual appropriateness was therefore supported through English-language administration, expert review, pilot testing and current-sample reliability estimates. This approach supports use in the present setting but does not replace a full Nigerian construct-validation study.

Measurement of clinical skills

Clinical skills were assessed under Objective Structured Clinical Examination conditions using procedure-performance checklists based on the Nursing and Midwifery Council of Nigeria format. Eight clinical instructors who were nurse educators, four from each participating school, conducted the assessments. Four curriculum-based procedures were selected by balloting and used at both assessments: taking vital signs, care of pressure areas, maintenance of oral hygiene and preparation of an unoccupied bed. The checklists assessed preparation, performance of the required sequence and completion or documentation activities. Each procedure carried 12 marks, giving 48 obtainable marks across the four procedures. Procedure scores and the aggregate clinical-skills score were converted to percentages for analysis.

Before data collection, the eight assessors completed a three-day standardisation workshop led by the principal investigator. Training covered checklist interpretation, station preparation, scoring of correctly, incorrectly and incompletely performed steps, and maintenance of consistent assessment conditions. Assessors practised scoring simulated performances and received a refresher briefing before the post-intervention assessment. Inter-rater reliability was examined through independent paired ratings of 16 nursing students outside the main sample. The overall absolute-agreement intraclass correlation coefficient was 0.90 (95% CI 0.84–0.94), with procedure-specific coefficients of 0.84–0.92. The experimental and control schools were located in different states and had no direct interaction during the study. Students and clinical instructors at each site were unaware of the identity, activities and results of the comparison school, thereby reducing the risk of information exchange and intervention contamination. Full assessor blinding to group allocation was not feasible because the clinical instructors worked within their respective schools; the study therefore used partial site-level blinding. The checklists also underwent professional review for face and content relevance.

The selected checklists were existing Nursing and Midwifery Council of Nigeria examination tools developed through professional input from experienced Nigerian nurse educators. For the present study, the eight instructors reviewed the four selected checklists for face validity, curriculum alignment, clarity and procedural relevance before administration. A numerical Content Validity Index was not available; this is reported transparently and considered when interpreting the measurement evidence.

Data-collection procedure

Administrative permission and ethical approval were obtained before recruitment. Students received study information and provided written informed consent. Pre-test assessment was conducted within the first week after consent using the same instruments in both groups. Following the six-week intervention, the post-test questionnaires and clinical assessments were scheduled for both schools within two months of the final session. Self-created identification codes were used to link pre-test and post-test records while protecting participant identity.

Data analysis

Data were analysed using IBM SPSS Statistics version 23. Frequencies and percentages summarised categorical variables, while means and standard deviations summarised continuous scores. Cronbach’s alpha assessed internal consistency of the LASSI using pre-intervention item-level responses. Baseline demographic distributions were compared using chi-square tests, and independent-samples t tests compared experimental and control groups at pre-test and post-test. Mean differences, two-tailed p values, 95% confidence intervals and standardised mean differences were reported for the principal group comparisons.

Before Pearson’s product–moment correlation was interpreted, normality was assessed using histograms, Q–Q plots and Shapiro–Wilk tests; linearity was examined with a scatterplot; and standardised residuals, leverage values and Cook’s distance were inspected for influential observations. Spearman’s rank correlation was conducted as a sensitivity analysis. Correlation magnitude was interpreted as very weak (0.00–0.19), weak (0.20–0.39), moderate (0.40–0.59), strong (0.60–0.79) or very strong (0.80–1.00). The exact two-tailed probability was calculated from the coefficient and sample size, and a 95% confidence interval was obtained using Fisher’s z transformation.

Hierarchical multiple linear regression was used to determine whether post-intervention learning strategies remained associated with post-intervention clinical skills after adjustment for group allocation, baseline clinical skills, baseline learning strategies, age and sex. The covariates were entered in Block 1 and post-intervention learning strategies in Block 2. Regression coefficients, standard errors, standardised beta coefficients, 95% confidence intervals, R², adjusted R² and change in R² were reported. Linearity, residual normality, homoscedasticity, multicollinearity and influential observations were examined. Possible ceiling effects were evaluated from observed score ranges and the proportions attaining the maximum or scoring at or above 90% of the obtainable score. Statistical significance was set at p-value (p) < 0.05.

Procedure-specific outcomes and all ten learning-strategy scales were reported separately to show whether the overall patterns were consistent across domains. A complete matrix of 40 scale-by-procedure correlations was not treated as confirmatory because the sample size was not planned for that number of comparisons and extensive testing would increase the risk of false-positive findings. The prespecified overall learning-strategy and overall clinical-skills scores therefore remained the primary correlational analysis.

Ethical considerations

Ethical approval was granted by the Zamfara State Health Research Ethics Committee (approval number ZSHREC03082021) on 16 August 2021. Administrative permission was obtained from the participating schools. Participation was voluntary, written informed consent was obtained, and confidentiality was maintained through coded records and secure data storage.

RESULTS

Participant characteristics and flow

Of the 190 Year Two students approached, 152 consented to participate and 38 did not proceed to consent. Systematic sampling selected 128 students for the study, while 24 consenting students were not selected after the required sample had been reached. All 128 selected participants completed both assessments and were analysed, comprising 64 students in each group. The groups were similar in sex, age, marital status and type of secondary school attended. In the experimental group, 54.7% were female, 51.6% were aged 18–22 years, 78.1% were unmarried and 56.3% had attended public secondary schools. The corresponding proportions in the control group were 51.6%, 54.7%, 82.8% and 51.6%. More than 93% of each group was reported as Hausa, indicating a highly homogeneous ethnic distribution.

The baseline socio-demographic characteristics of participants in the experimental and control groups are presented in Table 1. These characteristics were examined to determine whether the groups were comparable before the intervention.

Variable	Category	Experimental n (%)	Control n (%)	χ²	p-value
Sex	Male	29 (45.3)	31 (48.4)	0.141	0.707
	Female	35 (54.7)	33 (51.6)
Age (years)	18–22	33 (51.6)	35 (54.7)	0.265	0.966
	23–27	18 (28.1)	19 (29.7)
	28–32	10 (15.6)	9 (14.1)
	>33	3 (4.7)	1 (1.6)
Marital status	Unmarried	50 (78.1)	53 (82.8)	0.686	0.408
	Married	14 (21.9)	11 (17.2)
Secondary school	Private	28 (43.8)	31 (48.4)	0.772	0.380
	Public	36 (56.3)	33 (51.6)
Ethnic group	Hausa	60 (93.8)	62 (96.9)	0.675	0.714
	Yoruba	4 (6.3)	2 (3.1)
	Igbo	0 (0.0)	0 (0.0)

Table 1. Baseline socio-demographic characteristics of the experimental and control groups (n = 128).

Learning strategies

At baseline, the groups had comparable overall learning-strategy scores. The experimental-group mean was 44.2 ± 13.0 and the control-group mean was 46.4 ± 12.5, giving a mean difference of −2.2 points (95% CI −6.7 to 2.3; t(126) = −0.98, p = 0.331). Baseline component differences were also not statistically significant. At post-test, the experimental group had higher scores for skill, will, self-regulation and the overall learning-strategy measure. The overall post-test difference was 18.2 points (95% CI 14.4–22.0; t(126) = 9.59, p < 0.001; Cohen’s d = 1.70).

Changes in learning-strategy components between the experimental and control groups before and after the intervention are presented in Table 2. The table compares group differences across skill, will, self-regulation and overall learning-strategy scores.

Component	Pre: Exp. mean ± SD	Pre: Ctrl mean ± SD	Pre p-value	Post: Exp. mean ± SD	Post: Ctrl mean ± SD	Post p-value
Skill	50.2 ± 25.0	51.3 ± 24.4	0.802	75.0 ± 12.8	53.1 ± 24.8	<0.001*
Will	41.7 ± 22.4	42.3 ± 19.7	0.872	68.4 ± 14.6	43.7 ± 21.2	<0.001*
Self-regulation	51.2 ± 24.9	56.1 ± 28.3	0.300	72.6 ± 13.4	62.2 ± 21.9	0.002*
Overall	44.2 ± 13.0	46.4 ± 12.5	0.331	72.0 ± 8.0	53.8 ± 12.9	<0.001*
Note: Between-group p values are two-tailed independent-samples t-test results based on 64 participants per group; * = significant test.

Table 2. Learning-strategy components at pre-test and post-test by study group.

The post-intervention comparison of the ten learning-strategy scales is shown in Table 3. This analysis provides further detail on the specific learning behaviours contributing to differences between the experimental and control groups

Learning-strategy scale	Experimental mean ± SD	Control mean ± SD	t (df = 126)	p-value	Direction
Information processing	74.0 ± 13.3	50.8 ± 27.6	6.06	<0.001*	Exp. higher
Selecting main ideas	75.6 ± 12.7	54.5 ± 25.7	5.89	<0.001*	Exp. higher
Test strategies	75.1 ± 12.4	54.1 ± 21.2	6.84	<0.001*	Exp. higher
Attitude	67.4 ± 13.4	31.3 ± 16.5	13.59	<0.001*	Exp. higher
Motivation	67.5 ± 15.0	41.9 ± 25.2	6.98	<0.001*	Exp. higher
Anxiety management	70.2 ± 15.3	57.9 ± 21.9	3.68	<0.001*	Exp. higher
Time management	73.9 ± 14.6	68.0 ± 17.5	2.07	0.040*	Exp. higher
Concentration	73.1 ± 13.1	61.8 ± 21.0	3.65	<0.001*	Exp. higher
Use of academic resources	68.6 ± 12.9	59.8 ± 23.1	2.66	0.009*	Exp. higher
Self-testing	74.9 ± 12.8	59.2 ± 26.0	4.33	<0.001*	Exp. higher
* = significant test.

Table 3. Post-test scores for the ten learning-strategy scales by study group.

The post-test pattern was multidimensional. The largest between-group contrast occurred for attitude, while statistically significant differences were also observed for information processing, selecting main ideas, test strategies, motivation, anxiety management, time management, concentration, use of academic resources and self-testing. The findings therefore indicate that the group difference was not confined to one learning behaviour.

The LASSI demonstrated satisfactory internal consistency in the current sample. Cronbach’s alpha coefficients for the ten scales ranged from 0.74 to 0.86. The skill, will and self-regulation components produced coefficients of 0.89, 0.87 and 0.90, respectively, while the complete instrument coefficient was 0.93.

Clinical skills

The experimental and control groups did not differ significantly in the overall clinical-skills score at baseline (43.9 ± 6.5 versus 45.0 ± 4.8; mean difference −1.1, 95% CI −3.1 to 0.9; t(126) = −1.09, p = 0.278). Procedure-specific baseline scores were not uniform: the control group scored higher in taking vital signs and care of pressure areas, while the groups did not differ significantly in maintenance of oral hygiene or preparation of an unoccupied bed. This pattern is consistent with the non-randomised allocation of intact schools and requires caution when interpreting procedure-specific post-test differences.

At post-test, the experimental group recorded higher scores in all four procedures and in the overall clinical-skills measure. The overall mean difference was 19.1 points (95% CI 16.6–21.6; t(126) = 15.19, p < 0.001; Cohen’s d = 2.69). The consistency of the post-test direction across procedures indicates that the difference extended across several fundamental nursing activities.

The clinical-skills performance of participants across the four assessed nursing procedures is presented in Table 4.

Clinical procedure	Pre: Exp. mean ± SD	Pre: Ctrl mean ± SD	Pre p-value	Post: Exp. mean ± SD	Post: Ctrl mean ± SD	Post p-value
Taking vital signs	37.8 ± 14.4	42.1 ± 8.0	0.039*	73.8 ± 13.0	58.1 ± 11.9	<0.001*
Care of pressure areas	28.0 ± 10.4	45.4 ± 7.4	<0.001*	78.6 ± 12.0	61.9 ± 11.1	<0.001*
Maintenance of oral hygiene	48.0 ± 12.2	44.7 ± 9.3	0.088	80.8 ± 12.0	58.9 ± 12.2	<0.001*
Preparation of an unoccupied bed	51.9 ± 18.3	47.7 ± 7.6	0.092	80.1 ± 13.1	58.2 ± 11.5	<0.001*
Overall clinical skills	43.9 ± 6.5	45.0 ± 4.8	0.278	78.4 ± 6.7	59.3 ± 7.5	<0.001*
* = significant test.

Table 4. Clinical-skills scores at pre-test and post-test by procedure and study group.

The Table 4 compares pre-test and post-test scores between the experimental and control groups for each clinical procedure and the overall clinical-skills measure.

Assessment of possible ceiling effects

Observed ranges and upper-bound frequencies did not indicate a substantial ceiling effect for the overall measures. Overall learning-strategy scores ranged from 52.0 to 91.0 in the experimental group and from 29.0 to 82.0 in the control group; no participant attained the maximum score and only one experimental-group participant (1.6%) scored at or above 90%. Overall clinical-skills scores ranged from 62.5 to 93.8 and from 41.7 to 77.1, respectively; no participant attained the maximum overall score and three experimental-group participants (4.7%) scored at or above 90%. Modest upper-end clustering was observed for maintenance of oral hygiene and preparation of an unoccupied bed, for which 21.9% of experimental-group participants scored at or above 90%.

The distribution of post-intervention scores and assessment of potential ceiling effects are presented in Table 5. This analysis was conducted to determine whether high scores limited the ability of the measures to differentiate participant performance.

Outcome	Exp. range	Exp. ≥90% n (%)	Exp. max n (%)	Control range	Control ≥90% n (%)	Control max n (%)
Overall learning strategies	52.0–91.0	1 (1.6)	0 (0.0)	29.0–82.0	0 (0.0)	0 (0.0)
Overall clinical skills	62.5–93.8	3 (4.7)	0 (0.0)	41.7–77.1	0 (0.0)	0 (0.0)
Taking vital signs	41.7–100.0	7 (10.9)	2 (3.1)	33.3–83.3	0 (0.0)	0 (0.0)
Care of pressure areas	50.0–100.0	11 (17.2)	3 (4.7)	33.3–83.3	0 (0.0)	0 (0.0)
Maintenance of oral hygiene	50.0–100.0	14 (21.9)	4 (6.3)	25.0–83.3	0 (0.0)	0 (0.0)
Preparation of an unoccupied bed	41.7–100.0	14 (21.9)	5 (7.8)	33.3–91.7	1 (1.6)	0 (0.0)
Note: Scores at or above 90% of the obtainable score were classified as near the upper boundary.

Table 5. Post-intervention score ranges and assessment of possible ceiling effects.

Relationship between learning strategies and clinical skills

The learning-strategy and clinical-skills scores were approximately normally distributed on visual inspection. Shapiro–Wilk tests were not statistically significant for learning strategies (W = 0.986, p = 0.189) or clinical skills (W = 0.983, p = 0.104). The scatterplot showed an approximately linear positive relationship. Standardised residuals ranged from −2.72 to 2.61 and the maximum Cook’s distance was 0.074, indicating no influential outliers.

Across all 128 participants, learning strategies were moderately and positively correlated with clinical skills, r(126) = 0.556, 95% CI 0.423–0.665, p < 0.001. The Spearman sensitivity analysis produced a comparable association (rho = 0.548, p < 0.001), indicating that the finding was not materially dependent on distributional assumptions. The null hypothesis of no statistically significant relationship was rejected. In the unadjusted standardised model, learning strategies explained 30.9% of the observed variance in clinical-skills scores, F(1,126) = 56.38, p < 0.001.

The relationship between learning strategies and clinical skills across all participants is presented in Table 6. The table summarises the correlation coefficient and unadjusted regression model examining the association between the two main study variables.

Predictor	Outcome	r / β	95% CI for r	R²	Adjusted R²	F (df)	p-value
Learning strategies	Clinical skills	0.556	0.423–0.665	0.309	0.304	56.38 (1,126)	<0.001*
Note: In a simple one-predictor standardized model, β equals Pearson’s r. This model is unadjusted and should not be interpreted as establishing causality. *=significant test.

Table 6. Correlation and unadjusted standardised regression relating learning strategies to clinical skills (n = 128).

Adjusted multivariable analysis

The hierarchical regression model was statistically significant, F(6,121) = 51.86, p < 0.001, and explained 72.0% of the variance in post-intervention clinical-skills scores (R² = 0.720; adjusted R² = 0.706). Experimental-group membership was the strongest predictor (B = 13.72, β = 0.57, 95% CI 10.57–16.87, p < 0.001). After adjustment for group allocation, baseline clinical skills, baseline learning strategies, age and sex, post-intervention learning strategies remained positively associated with clinical skills (B = 0.23, β = 0.27, 95% CI 0.12–0.34, p < 0.001). Adding post-intervention learning strategies explained a further 3.9% of the variance (ΔR² = 0.039, F-change(1,121) = 16.85, p < 0.001). Baseline clinical skills and age were also significant, whereas baseline learning strategies and sex were not.

The adjusted predictors of post-intervention clinical-skills performance are presented in Table 7. The hierarchical regression model examines whether learning strategies remained associated with clinical skills after controlling for group allocation and relevant participant characteristics.

Predictor	B	SE	β	t	p-value	95% CI for B
Post-intervention learning strategies	0.23	0.055	0.27	4.18	<0.001*	0.12–0.34
Experimental group	13.72	1.59	0.57	8.63	<0.001*	10.57–16.87
Baseline clinical skills	0.20	0.069	0.17	2.90	0.004*	0.06–0.34
Baseline learning strategies	0.03	0.037	0.05	0.81	0.419	−0.04–0.10
Age group	1.28	0.52	0.14	2.46	0.015*	0.25–2.31
Female sex	0.62	1.05	0.03	0.59	0.556	−1.46–2.70
Note: Control group = 0 and experimental group = 1; male = 0 and female = 1. Model R² = 0.720; adjusted R² = 0.706.*=significant test

DISCUSSION

The present study examined the relationship between learning strategies and clinical skills among nursing students in North-West Nigeria and compared scores between experimental and control groups. Three principal findings emerged. First, the groups had comparable overall learning-strategy and clinical-skills scores at baseline, although two procedure-specific baseline differences were observed. Second, the experimental group recorded higher post-test learning-strategy and clinical-skills scores than the control group. Third, learning strategies were moderately and positively associated with clinical skills across the 128 participants.

The baseline learning-strategy findings indicate that both groups began with similar overall strategic-learning profiles. The experimental-group mean was 44.2, compared with 46.4 in the control group, and the confidence interval for the difference included zero. This comparability strengthens the interpretation of the post-test contrast for the overall learning-strategy score, while the use of intact schools still precludes the assumption of complete equivalence. At post-test, the experimental group’s overall score exceeded that of the control group by 18.2 points, with a large standardized difference. The result indicates that the structured educational programme was accompanied by a substantial difference in measured learning strategies within the study setting.

The post-test difference extended across skill, will and self-regulation. Higher skill scores suggest stronger information processing, identification of central ideas and preparation for assessment. These processes are relevant to clinical learning because students must distinguish critical procedural information, connect principles with practice and recall an appropriate sequence of care. Higher self-regulation scores suggest greater use of time management, concentration, academic resources and self-testing. Such strategies may help students organise practice, identify gaps in understanding and seek corrective feedback before formal assessment.

The will component remained lower than the skill and self-regulation components in the experimental group. This pattern may indicate that attitudes, motivation and management of learning-related anxiety require more sustained reinforcement than cognitive and organisational strategies. Strategy instruction can provide immediate techniques for planning and information processing, whereas motivational change may depend on repeated mastery experiences, supportive feedback and continued clinical supervision. The interpretation remains tentative because the study assessed outcomes within two months of the intervention.

The learning-strategy findings are broadly consistent with evidence showing that self-regulated learning supports nursing students’ engagement with clinical practice [6–9]. They also accord with intervention research in which structured reflection, assessment and critical-thinking activities strengthened self-regulation and clinical performance [10]. However, the present study differs from research that measures learning styles or self-perceived competence [11]. By assessing ten strategy scales and directly observed procedures, it provides evidence that strategic learning is relevant to demonstrated rather than perceived clinical performance.

The overall clinical-skills scores were comparable at baseline, but the procedure-specific results require a more qualified interpretation. The control group had higher baseline scores for taking vital signs and care of pressure areas. These imbalances are plausible in a non-equivalent group design because the schools may have differed in prior teaching sequence, practice exposure or assessment familiarity. Consequently, the post-test procedure differences cannot be attributed solely to the intervention without adjustment for baseline performance. The overall post-test difference was nevertheless large and occurred in the same direction across all four procedures.

The four procedures represented complementary dimensions of fundamental nursing care. Taking vital signs required systematic observation and recording of physiological measurements. Care of pressure areas required preventive care, patient positioning, skin assessment and maintenance of tissue integrity. Maintenance of oral hygiene assessed organised personal care, patient comfort and infection-prevention practices. Preparation of an unoccupied bed required sequencing, environmental organisation and adherence to basic care standards. The consistently higher post-test scores in the experimental group indicate that the observed difference was not limited to one procedural domain.

The high post-test means did not correspond to a substantial ceiling effect for the overall learning-strategy or clinical-skills measures, because very few students scored at or above 90% and none attained the maximum overall score. Modest upper-end clustering occurred for oral hygiene and unoccupied-bed preparation. The limited number of scoring points on individual checklists may therefore have reduced discrimination among some high-performing students, and procedure-specific findings should be interpreted alongside the aggregate clinical-skills score.

The principal finding relevant to the title was the moderate positive relationship between learning strategies and clinical skills. The Pearson coefficient of 0.556 and the comparable Spearman coefficient of 0.548 indicate that students with stronger learning-strategy scores tended to demonstrate stronger clinical skills. The confidence interval excluded weak and null relationships, and the unadjusted model accounted for approximately 31% of the variance in clinical-skills scores. This magnitude is educationally meaningful while also showing that substantial variation remained attributable to other influences and measurement error. The association can be interpreted through the functional requirements of clinical performance. Students must retrieve theoretical principles, organise procedural information, maintain concentration, monitor the accuracy of their actions and evaluate outcomes. Information processing and selection of main ideas may support recognition of critical steps. Time management and concentration may facilitate deliberate practice, while self-testing and resource use may help students detect weaknesses and seek assistance. Stronger learning strategies may therefore support more systematic engagement with clinical tasks, although the association does not establish a causal pathway.

The finding is consistent with self-regulated learning theory [4,5]. Clinical-skill acquisition involves forethought, performance monitoring and reflection. During forethought, learners set objectives and plan their approach. During performance, they regulate attention and monitor action. During reflection, they interpret feedback and identify further learning needs. The positive relationship observed in the present study supports the theoretical expectation that deliberate regulation of learning corresponds with stronger performance in complex applied tasks.

The observed group differences, correlation and adjusted regression answer related but distinct questions. The group comparisons describe score differences between the experimental and control schools, whereas the correlations describe how the two title variables varied together across participants. In the multivariable model, learning strategies remained independently associated with clinical skills after adjustment for group allocation, baseline clinical skills, baseline learning strategies, age and sex. The reduction from the crude correlation to the adjusted standardised coefficient indicates that group and baseline characteristics explained part of the unadjusted relationship. Experimental-group membership remained the strongest predictor. Because each group was located in a different school, the group coefficient represents a combined intervention-and-institution effect; residual institutional confounding cannot therefore be excluded.

The findings have practical implications for nursing education. Strategy development may be integrated with clinical instruction through explicit goal setting, procedural planning, scheduled practice, self-testing, reflective review and structured feedback. These activities may be incorporated into existing supervision rather than delivered as a separate resource-intensive programme. In low-resource settings, small-group coaching and peer practice could contribute to more deliberate use of available clinical opportunities, but feasibility, instructor workload and implementation fidelity should be evaluated.

Overall, the evidence indicates that learning strategies have relevance beyond classroom assessment. Their association with directly observed clinical skills suggests that cognitive, motivational and self-regulatory support may contribute to the development of practice readiness. The findings provide a basis for further controlled, multicentre research that uses verified psychometric scoring, blinded or standardised clinical assessment and adjusted analyses.

Implications for nursing education

Learning-strategy assessment may help educators identify students who require additional support with information processing, motivation, time management, concentration, self-testing or use of learning resources.

Clinical instruction could combine demonstration with learner-generated goals, guided practice, immediate feedback and reflective review.

Strategy-focused support should be evaluated as a complement to routine clinical teaching rather than as a replacement for supervised practice.

Implementation studies should examine the feasibility, instructor workload, fidelity and sustainability of small-group academic coaching in public nursing schools.

Clinical-skills assessment should use clearly documented scoring rubrics and assessor-standardisation procedures to strengthen reliability.

Strengths and Limitations

A major strength was the use of directly observed clinical procedures rather than exclusive reliance on students’ perceptions of competence. The study also assessed multiple dimensions of learning strategies, used a control group, obtained complete follow-up and included procedure-specific outcomes. The corrected correlation is reported with a confidence interval and an exact significance calculation consistent with n = 128.

Several limitations should be considered. The quasi-experimental design used intact schools without individual randomisation, creating potential selection bias and institutional confounding. Because the experimental and control groups were located in different schools, coaching exposure could not be separated completely from institutional characteristics. Procedure-specific baseline imbalance was evident for taking vital signs and care of pressure areas. Learning strategies were self-reported and may have been affected by social-desirability or response bias. Assessors could not be blinded to school or assessment period. A numerical Content Validity Index was not available for the clinical checklists, and a full Nigerian construct-validation study of the 64-item LASSI was not identified. The sample was drawn from two public schools and was ethnically homogeneous, limiting generalisability. Although no substantial ceiling effect was identified for the overall outcomes, modest upper-end clustering in oral hygiene and unoccupied-bed preparation may have reduced discrimination among higher-performing students. Residual confounding may remain despite statistical adjustment.

CONCLUSION

Learning strategies were moderately and positively associated with clinical skills among nursing students in North-West Nigeria. The association remained statistically significant after adjustment for group allocation, baseline scores, age and sex. The experimental group also recorded higher post-test learning-strategy and clinical-skills scores than the control group. Strategy-focused support may therefore contribute to clinical learning when integrated with supervised practice; however, the non-randomised school-level design, residual institutional confounding and modest procedure-specific upper-end clustering require cautious interpretation and further multicentre evaluation.

Ethics approval and consent to participate

Ethical approval was granted by the Zamfara State Health Research Ethics Committee (approval number ZSHREC03082021) on 16 August 2021. Administrative permission was obtained from both schools. All participants provided written informed consent.

Data availability

De-identified data may be made available by the corresponding author upon reasonable request and subject to institutional and ethical approval.

Competing interests

No competing interests.

Funding

The research received institutional support through the Tertiary Education Trust Fund. No separate funding was received for preparation of this manuscript.

Author contributions

Y.L. contributed to conceptualisation, methodology, intervention development, investigation, formal analysis, project administration, funding acquisition and preparation of the original draft. M.M.M contributed to investigation, data collection, validation and review and editing of the manuscript. A.S. contributed to methodology, clinical-content validation, interpretation and review and editing. A.S.K. contributed to supervision, project administration, interpretation and review and editing. All authors reviewed and approved the final manuscript. M.Z contributed in data collection, validation and review and editing of the manuscript.

Previous dissemination

The study was undertaken within a doctoral research programme. The analyses and tables in this manuscript have not been published previously and that this is the first journal report focusing on learning strategies and directly observed clinical skills across both study groups.

Acknowledgements

The authors acknowledge the participating nursing students, clinical instructors, research assistants, school administrations and academic supervisors who supported the conduct of the study.

REFERENCES

1. American Association of Colleges of Nursing. The Essentials: Core Competencies for Professional Nursing Education. Washington, DC: AACN; 2021.
2. World Health Organization. Nurse Educator Core Competencies. Geneva: World Health Organization; 2016.
3. Weinstein CE, Palmer DR, Schulte AC. Learning and Study Strategies Inventory (LASSI). Clearwater, FL: H & H Publishing; 1987.
4. Boekaerts M. Self-regulated learning: where we are today. International Journal of Educational Research. 1999;31(6):445–457. doi:10.1016/S0883-0355(99)00014-2.
5. Zimmerman BJ. Becoming a self-regulated learner: an overview. Theory Into Practice. 2002;41(2):64–70. doi:10.1207/s15430421tip4102_2.
6. Kurt E, Eskimez Z. Examining self-regulated learning of nursing students in clinical practice: a descriptive and cross-sectional study. Nurse Education Today. 2022;109:105242. doi:10.1016/j.nedt.2021.105242.
7. Kim SH. The mediating effect of self-regulated learning on the relationships among emotional intelligence, collaboration, and clinical performance in Korean nursing students. Journal of Nursing Research. 2022;30(3):e212. doi:10.1097/JNR.0000000000000494.
8. Subaş F, Karacay P. Factors associated with nursing students’ self-regulated learning in clinical practicum: a descriptive cross-sectional study. Nursing & Health Sciences. 2023;25(1):73–82. doi:10.1111/nhs.12997.
9. Tanimura C, Okuda R, Tokushima Y, Matsumoto Y, Katou S, Miyoshi M. Examining the reliability and validity of a self-regulated learning strategy scale for undergraduate nursing students and effective factors of self-regulated learning strategies. Nurse Education Today. 2023;128:105872. doi:10.1016/j.nedt.2023.105872.
10. Zeng W, Goh YX, Ponnamperuma G, Liaw SY, Lim CC, Jayarani P, Chia SY, de Bruin A. Promotion of self-regulated learning through internalization of critical thinking, assessment and reflection to empower learning (iCARE): a quasi-experimental study. Nurse Education Today. 2024;142:106339. doi:10.1016/j.nedt.2024.106339.
11. Mousavi SK, Javadzadeh A, Hasankhani H, Alijani Parizad Z. Relationship between learning styles and clinical competency in nursing students. BMC Medical Education. 2024;24:469. doi:10.1186/s12909-024-05432-z.
12. Irvine S, Brooks I, Lau R, McKenna L. Self-regulated learning instructional support for students enrolled in an accelerated nursing program. Collegian. 2020;27(4):402-409. doi:10.1016/j.colegn.2019.11.007
13. Sadeghi N, Janatolamakn M, Rezaeian S, Rashi M, Khatony A. Exploring self-directed learning readiness and related factors: the role of time management skills in nursing students. BMC Medical Education. 2024;24:1088. doi:10.1186/s12909-024-06083-w.
14. Rosner B. Fundamentals of Biostatistics. 8th ed. Boston, MA: Cengage Learning; 2015.
15. Swartz SL, Prevatt F, Proctor BE. A coaching intervention for college students with attention-deficit/hyperactivity disorder. Psychology in the Schools. 2005;42(6):647-656. doi:10.1002/pits.20101.
16. Anarado AN, Agu GU, Nwonu EI. Factors hindering clinical training of students in selected nursing educational institutions in Southeastern Nigeria. Nurse Education Today. 2016;40:140–145. doi:10.1016/j.nedt.2016.02.022.
17. Akinpelu AO, Oyewole OO, Odunaiya N, Odole AC, Olley JP. Clinical students’ perception of educational environment in a Nigerian university: a mixed-method study. BMC Medical Education. 2024;24:725. doi:10.1186/s12909-024-05734-2.
18. Ogunmuyiwa AO, Anyama SC, Howells BB, Ogunleye CA, Esan DT. Gender and age differential on competence level in clinical practice among nursing students in a tertiary institution in Nigeria. SAGE Open Nursing. 2025;11:23779608251331587. doi:10.1177/23779608251331587.