The effect of simulation-based laboratory training on undergraduate nursing students’ clinical skill, satisfaction, and self-confidence

Abstract

Background

The increasing complexity of healthcare highlights the need for experiential, evidence-based learning in nursing education. Simulation-based training offers a safe space to practice clinical skills and prepare for real-world scenarios. The present study aims to evaluate the effect of simulation-based laboratory training on undergraduate nursing students’ clinical skills, satisfaction, and self-confidence compared to those who did not receive such training.

Methods

This prospective, parallel-group, non-randomized controlled trial was conducted with 180 undergraduate nursing students in their in their 2nd, 3rd, and 4th academic years. Participants were selected using a stratified sampling method, with 90 students assigned to the intervention group and 90 to the control group. The control group received standard theoretical instruction, whereas the intervention group underwent additional training in a well-equipped simulation education laboratory. The training focused on five core nursing skills: colostomy care, insulin pen administration, nasogastric tube insertion, postpartum hemorrhage assessment, and mental status examination. Each student practiced the procedures until independent, accurate performance, and assessments were conducted at baseline, one week, and one month post-intervention. Data were collected using demographic characteristics form, skill checklists, and student satisfaction and self-confidence in learning questionnaire. Statistical analyses were performed using R software, and the Mann–Whitney U test was used to compare groups.

Results

Pre-intervention skill scores were comparable between groups (p > 0.05). The intervention group showed significantly higher clinical skill scores at one week and one month (p < 0.001). Satisfaction (p = 0.560) and self-confidence (p = 0.296) did not differ significantly between groups.

Conclusion

Simulation-based training enhances clinical skill acquisition and retention in nursing students. However, it does not significantly impact satisfaction or self-confidence, indicating a need for additional strategies to support holistic learning.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-025-04004-w.

Introduction

Nursing education plays a pivotal role in preparing future healthcare professionals capable of providing safe, high-quality, and evidence-based care in increasingly complex clinical environments. As the largest segment of the healthcare workforce, nurses are responsible for ensuring patient safety, coordinating care, and promoting optimal health outcomes [1, 2]. In this regard, practical experience in health care settings is the foundation of nursing education [3]. Therefore, nursing curricula must bridge the gap between theoretical instruction and clinical practice by fostering students’ critical thinking, clinical reasoning, and psychomotor skills through experiential learning approaches [1, 4, 5].

Simulation-based laboratory training has emerged as a promising pedagogical tool to bridge the theory–practice gap [2, 6]. It offers a safe, structured, and controlled environment that allows students to apply theoretical knowledge, refine technical and decision-making skills, and gain confidence before encountering real patients [2, 7, 8]. High-fidelity simulations, in particular, have been shown to improve students’ competence, reduce anxiety, and enhance self-confidence and satisfaction during clinical practice [9–13]. This experiential form of learning is particularly valuable in nursing, where the stakes are high and the margin for error can be minimal. Moreover, the repetitive nature of simulation encourages mastery of skills, promoting both self confidence and proficiency in clinical practice [9].

Despite the growing global integration of simulation-based education in nursing curricula, evidence regarding its impact on key educational outcomes—such as clinical skill, student satisfaction, and self-confidence—remains inconsistent and context-dependent. Existing studies predominantly focus on short-term skill acquisition or learner perceptions, with limited research adopting a comprehensive, experimental design that examines these outcomes collectively. Moreover, few studies assess the medium-term effects of simulation training, particularly among undergraduate nursing students in controlled settings. This study addresses these gaps by evaluating the short- and medium-term effects of simulation-based laboratory training on clinical performance, satisfaction, and self-confidence through a structured and comprehensive design. The findings are expected to offer a more integrated understanding of how simulation influences both cognitive and affective learning domains, thereby informing evidence-based instructional strategies for enhancing clinical skill in undergraduate nursing education.

Aim

The primary aim of the study is to assess the clinical skill of students who receive training in a high-fidelity simulation training laboratory versus those who do not. Additionally, the study evaluates students’ satisfaction and self-confidence levels as secondary objectives.

Hypotheses:

H₁

Simulation-based laboratory training has a significant effect on undergraduate nursing students’ clinical skill.

H₀

Simulation-based laboratory training has no significant effect on undergraduate nursing students’ clinical skill.

Method

Design

This is a prospective parallel group, non-randomized controlled study with posttest-only control group design.

Participants and setting

The sample size was determined using the G*Power 3.1 software based on an independent samples t-test (two-tailed), with an alpha level of 0.05 and statistical power set at 95% to minimize the risk of Type I errors and to ensure adequate sensitivity to detect significant differences. A large effect size (Cohen’s d = 0.790), derived from prior research with a similar educational intervention [14] was used. This approach was chosen because Cohen’s d is a well-established metric for parametric test-based power analyses. The minimum required sample size was calculated to be 36 participants per group.

To enhance generalizability and statistical power, a total of 180 undergraduate nursing students were recruited, with 90 participants assigned to the experimental group and 90 to the control group. The sample included equal numbers of second-, third-, and fourth-year students (30 from each year per group).

The study was conducted with 2nd-, 3rd-, and 4th-year undergraduate nursing students at a public university between March and May 2024. These students were selected because they were actively enrolled in clinical skills courses and had completed the theoretical components of nursing fundamentals, making them suitable for simulation-based skill training. Separate announcements were made to each class level, and students who expressed interest in participating were invited to attend an informational meeting. During these meetings, the purpose, procedures, and voluntary nature of the study were explained in detail, and informed consent was obtained from all participants. Students who were over 18 years old, willing to participate in the study, and able to speak and understand Turkish were included in the study. Students who had previously received any training related to the intervention, were foreign nationals, were not enrolled in a course, or were in their first year of study were excluded from the study. No monetary or academic incentives were provided to participants.

Allocation concealment

A non-probability, stratified sampling method was employed based on students’ existing academic levels (2nd, 3rd, and 4th year). Within each academic level, students were divided into two groups using a computerized random number generator: one assigned to the experimental group and the other to the control group. Although a computerized allocation method was used, randomization at the individual level was not applied. The sample was drawn from existing student cohorts, and the allocation process followed a systematic but non-random approach. The first 30 students on each sublist were selected per group and year level. If a student did not meet the inclusion criteria, the next eligible student on the list was selected. Out of the initially screened participants, six students were excluded for not meeting inclusion criteria, and ten declined to participate, resulting in a final enrolled sample of 180 students. The flow diagram detailing participant inclusion, allocation, and analysis has been included in the Fig. 1 to enhance transparency and replicability.

Fig. 1.

Flow diagram

Intervention

Recruitment process

In both groups, a descriptive characteristics form was administered during the initial meeting after obtaining informed consent from the students. Subsequently, all students received a standardized theoretical education session lasting approximately 60 min, delivered in lecture format by faculty members experienced in simulation-based education. Immediately after the theoretical training, all students underwent a baseline assessment of their clinical skills using the Objective Structured Clinical Examination (OSCE). The results obtained constituted the baseline clinical skill levels of the groups. On the same day, as a strategy to prevent information exchange between groups, control group students were dismissed after the OSCE assessment, while the intervention group received the simulation-based training in the high-fidelity laboratory. During these sessions, students practiced each assigned procedure under the supervision of expert instructors. Each student repeated the procedures until they were able to perform them accurately and independently, in accordance with the predefined competency checklists. This approach ensured mastery of the clinical skills. No formal debriefing process was conducted following the simulations, as the instructional strategy focused on guided.

Post-intervention OSCE assessments were conducted at two intervals: one week and one month after the training. During these evaluations, students were asked to independently perform the procedures at each OSCE station without guidance. Trained evaluators, blinded to group allocation, scored student performance using the same Skill Checklist used at baseline. At the one-month post-intervention point, all participants were asked to complete the Student Satisfaction and Self-Confidence in Learning Scale to evaluate their perceptions of the training. A separate researcher, who remained blinded to group assignments throughout the study, conducted all follow-up interviews and coordinated data collection. Figure 1 presents a schematic timeline of the intervention, including educational sessions, assessment points, and outcome measures.

Content of education and training

The theoretical education and simulation-based training content was structured to be appropriate for the students’ level and to reflect the most commonly encountered situations in clinical practice. In this context, meetings were held with expert faculty members from each academic year, during which case selections and practice scenarios were developed based on the nursing curriculum. Additionally, an educational plan was created in accordance with national and international nurse education schedules focusing on priority situations frequently encountered in clinical practice. Accordingly, the training content was structured in alignment with the learning objectives of each academic year. Second-year students were trained in colostomy care and insulin pen administration; third-year students focused on nasogastric tube insertion and postpartum hemorrhage assessment; while fourth-year students were instructed in conducting mental status examinations.

The training content and simulations were developed and implemented by expert educators in the fields of Surgical Nursing, Internal Medicine Nursing, Public Health Nursing, Women’s Health and Diseases Nursing, Pediatric Health and Diseases Nursing, and Mental Health and Psychiatric Nursing. The scenarios and practices were specifically tailored to reflect clinical situations encountered in these respective specialties, ensuring comprehensive and field-relevant skill development.

Data collection

Baseline data (T0) were collected on the day participants were enrolled in the study. Follow-up data were gathered one week (T1) and one month (T2) after the intervention through structured, face-to-face interviews conducted by trained researchers blinded to group allocation. Data collection adhered to a standardized protocol to ensure consistency across all time points.

Prior to the initiation of data collection, all researchers responsible for administering interviews and assessments received centralized training on the study protocol, data collection tools, and interview techniques. For OSCE evaluations, the two assessors participated in an additional calibration session focused on scoring consistency and checklist application to ensure inter-rater reliability throughout the assessment process. Coordination meetings were held periodically to maintain standardization across all procedures.

Outcome measurements and instruments

Outcome measurements

Primary Outcome: The primary outcome was clinical skill performance, assessed using the Skill Checklist at one week (T1) and one month (T2) post-intervention. The checklist provided a structured, objective measure of procedural skill.

Secondary Outcomes: Secondary outcomes included student satisfaction and self-confidence in learning, evaluated one month post-intervention (T2) using the Student Satisfaction and Self-Confidence in Learning Scale.

Measurement instruments

Demographic Characteristics Form: Developed by the researchers for the current study (Suppl. 1), this form included four sociodemographic questions (e.g., age, sex, high school and academic grade point).

Skill checklists

Five separate skill checklists (Suppl. 2) were developed by the research team for the current study to evaluate students’ performance in colostomy care, insulin pen administration, nasogastric tube insertion, postpartum hemorrhage assessment, and mental status examination. These checklists were prepared based on relevant literature [15–20] and expert opinions to ensure content validity and suitability for different academic levels. Each checklist consisted of 30 procedural steps. Items were scored as either “performed” (1 point) or “not performed” (0 points), and total scores represented the sum of accurately completed steps. For example, in the colostomy care checklist, the first step was “Washes hands”, which was scored as 1 if correctly performed and 0 if omitted. A similar binary scoring rubric was applied to all other steps (e.g., “Verifies the patient’s identity,” “Informs the patient about the procedure”).

Student satisfaction and self-confidence in learning questionnaire

Originally developed by Jeffries and Rizzolo (2006) [21] as a 13-item scale and adapted into Turkish by Ünver et al. (2017) [22], the Turkish validity and reliability study removed one item, resulting in a 12-item scale. this 12-item scale consists of two subscales: Satisfaction with Current Learning (5 items) and Self-Confidence in Learning (7 items). It uses a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Higher scores indicate higher levels of student satisfaction and self-confidence in learning. The Cronbach’s alpha coefficient for the original scale is 0.89 for the overall scale, 0.77 for satisfaction, and 0.85 for self-confidence. In this study, the Cronbach’s alpha coefficients were 0.95 for the overall scale, 0.95 for the satisfaction subdimension, and 0.92 for the self-confidence subdimension.

Validity and reliability

The content and face validity of the five skill checklists—developed to assess colostomy care, insulin pen administration, nasogastric tube insertion, postpartum hemorrhage assessment, and mental status examination—were evaluated by 10 experts in nursing education and clinical practice. Each item was assessed for clarity, relevance, and alignment with clinical competencies. Based on expert feedback, necessary revisions were made to enhance the educational and clinical suitability of the items. The item-level Content Validity Index (I-CVI) values ranged from 0.70 to 0.90, indicating acceptable to high content validity.

A pilot study was conducted with 10 nursing students to evaluate the reliability and applicability of the finalized checklists. Internal consistency, assessed via Cronbach’s alpha, ranged from 0.81 to 0.92, reflecting high reliability. Inter-rater reliability was determined by two independent observers scoring student performances, yielding an Intraclass Correlation Coefficient (ICC) of 0.91 (95% CI: 0.85–0.95), which indicates excellent agreement. Students involved in the pretesting phase were excluded from the main study to avoid potential bias.

Data analysis

Statistical analysis was conducted using R software (R Foundation for Statistical Computing, Vienna, Austria), utilizing packages such as tidyverse and finalfit. Categorical variables were presented as percentages and frequencies, while continuous variables were presented as frequency, mean and median. The normality of the data distribution was assessed using the Shapiro-Wilk test. The results indicated that the primary and secondary outcome variables were not normally distributed, supporting the use of non-parametric tests (W = 0.984, p = 0.034 for before training; W = 0.761, p = 0.000 for one week after training; W = 0.938, p = 0.001 for one month after training; W = 0.975, p = 0.00 for satisfaction with the teaching method; W = 0.949, p = 0.001 for self-confidence in learning). For primary and secondary outcomes, statistical analyses were performed using the Mann–Whitney U test for unpaired groups. In addition to p-values, effect sizes were calculated using the rank-biserial correlation coefficient (r) to quantify the magnitude of group differences. According to Cohen’s classification, values of r between 0.10–0.29 indicate a small effect, 0.30–0.49 a medium effect, and 0.50 or above a large effect [23].

Ethical considerations

Ethical approval for the study was granted by the university ethics committee (approval number: 2023-86/E-13562490-050.01.04-436200, date: October 25, 2023). Written informed consent was obtained from all participants prior to data collection. Participation was voluntary, and confidentiality and anonymity of the participants were strictly maintained throughout the study, in accordance with the principles of the Declaration of Helsinki.

Results

The study was completed with 90 undergraduate nursing students of the intervention group and 90 in the control group students. The flow diagram of the study is shown in Fig. 1.

Sample characteristics

Table 1 provides sociodemographic characteristics, as well as baseline measurements, did not differ significantly between the groups (p > 0.05).

Table 1.

Demographic characteristics of the students at baseline

Characteristics		Total group (n = 180)	Intervention group (n = 90)	Control group (n = 90)	Statistical analyses
Age		20 (20–22)	21 (20–22)	21 (20–22)	U = 3838.000 p = 0.532
Sex	Female	153 (85)	72 (80)	81 (90)	x2 = 3.529 p = 0.094
	Male	27 (15)	18 (20)	9 (10)
High school	Anatolian	130 (72.2)	61 (67.8)	69 (76.6)	x2 = 2.860 p = 0.239
	Science	29 (16.1)	15 (16.7)	14 (15.6)
	Other	21 (11.7)	14 (15.5)	7 (7.8)
Academic grade point		3.11 ± 0.36	3.14 ± 0.30	3.08 ± 0.42	t = 0.974 p = 0.331
Clinical skill score after theoretical education		15 (11–18)	15 (10–19)	16 (11.2–18)	U = 3646.500 p = 0.247

Data was presented as median (Q1-Q3), n (%), and mean ± SD; Q1, first quartile; Q3,third quartile; SD, Standart deviation

Primary outcomes

The median (Q1–Q3) skill scores one week after training were 23 (19–25) in the intervention group and 15.5 (12–19) in the control group. One month after the intervention, the median skill scores were 23 (15.2–27) in the intervention group and 18 (14–22) in the control group. Clinical skill scores were significantly higher in the intervention group both one week and one month after training (p < 0.001 for both comparisons), with a large effect size at one week (r = 0.525) and a medium effect size at one month (r = 0.304) (Fig. 2; Table 2).

Fig. 2.

Skill scores in the intervention and control group

Table 2.

Comparison of primary and secondary outcomes between the groups

Variables	Total Group (n = 180) Median (Q1-Q3)	Intervention group (n = 90) (Median Q1-Q3)	Control group (n = 90) Medin (Q1-Q3)	Statistical analyses	Effect size
Primar Outcomes
One week after training	19 (14.8–24)	23 (19–25)	15.5 (12–19)	U = 1588.500 p < 0.001	0.525
One month after training	20 (15–26)	23 (15.2–27)	18 (14–22)	U = 2625.000 p < 0.001	0.304
Secondary Outcomes
Satisfaction with the teaching method	4.8 (4–5)	4.6 (4–5)	5 (4–5)	U = 4242.000 p = 0.560	0.043
Self-Confidence in learning	4.4 (4–5)	4.4 (4-4.9)	4.5 (4–5)	U = 4132.500 p = 0.292	0.078

Q1, first quartile; Q3, third quartile

Secondary outcomes

The median satisfaction scores with the teaching method were 4.6 (4–5) for the intervention group and 5 (4–5) for the control group. The median self-confidence in learning scores were 4.4 (4–4.9) for the intervention group and 4.5 (4–5) for the control group. There were no statistically significant differences between the groups in terms of satisfaction with the teaching method and self-confidence in learning (p > 0.05), and the calculated effect sizes indicated negligible effects in both outcomes (Fig. 3; Table 2).

Fig. 3.

Comprasion of secondary outcomes between groups

Discussion

This study examined the effectiveness of simulation-based laboratory training in enhancing undergraduate nursing students’ clinical skill, satisfaction with the instructional method, and self-confidence in learning. The findings demonstrate that simulation training significantly improves clinical skills in both the short-term (one week) and the medium-term (one month), as evidenced by the increased OSCE performance scores in the intervention group. These results align with a substantial body of research emphasizing simulation’s role in facilitating skill acquisition in nursing education [2, 24]. The very high Cronbach’s alpha values observed for the Satisfaction (0.95) and Self-Confidence (0.92) subscales may reflect a degree of item redundancy or limited variability among responses. However, this may also be attributed to the homogeneity of the study population and the specific context of the educational intervention, where participants showed consistently positive perceptions.

Simulation-based laboratory training allows students to bridge the gap between theoretical instruction and real-life clinical practice. The ability to engage in repetitive, hands-on experience in a risk-free environment enhances the technical skills, critical thinking skills, satisfaction, knowledge, and the clinical skill of nursing students [25, 26]. The observed sustained improvement over one month in the intervention group suggests that simulation can enhance not only immediate learning but also medium-term skill retention. This finding supports previous research emphasizing the value of simulation exposure in developing the confidence, self-efficacy, and critical thinking skills of nursing students [5]. It is known that low- and high-fidelity mannequins both improve clinical performance, with high-fidelity models offering a stronger capable of realistic physiological responses to nursing students’ actions [27]. The sustained skill improvement observed in our study one month after training aligns with findings from [28], who reported that repeated simulation sessions significantly promotes proficiency and competency.

Despite the significant improvement in clinical skills observed in the intervention group, our study did not reveal statistically significant differences between groups in terms of satisfaction with the teaching method or self-confidence in learning. This result is partially consistent with the findings of Kim and Choi (2023), who reported no significant difference in learner confidence following either virtual simulation or simulation-based laboratory training [29]. Similarly, Kim (2024) found no meaningful change between pre- and post-intervention performance confidence levels, further supporting the notion that simulation-based instruction alone may not sufficiently influence affective outcomes such as self-confidence [30].

On the other hand, other studies have reported contrasting results. For example, standardized patient and virtual reality-based simulations have been associated with increased knowledge, self-confidence, and satisfaction among nursing students, along with reduced stress levels in clinical environments [31, 32]. Similarly, it has been shown that simulation-based practical education can enhance students’ self-efficacy and satisfaction with the learning process [30, 33]. Park and Lee (2022) also reported high levels of satisfaction with simulation training, suggesting that specific instructional designs and contextual factors may influence the effectiveness of simulation-based approaches in enhancing students’ satisfaction and confidence [34]. The absence of significant differences in affective outcomes such as satisfaction and self-confidence between the intervention and control groups may be attributed to several methodological and contextual factors. Differences in study design, simulation fidelity, instructional strategies, timing of data collection, and measurement tools likely contributed to inconsistencies across studies. For instance, satisfaction and self-canfidence scores in the present study were obtained using an instrument different from those used in previous research, which may have reduced the comparability of findings. Moreover, the very high Cronbach’s alpha values obtained for the Satisfaction (0.95) and Self-Confidence (0.92) subscales may also indicate item redundancy or limited response variability, thereby reducing the sensitivity of the scales to detect between-group differences [35, 36]. Affective changes may evolve gradually over time and may not be fully captured by immediate post-intervention assessments. Futhermore, the single-session design and short exposure period might have constrained students’ emotional engagement and limited the potential for attitudinal change. Prior research has shown that affective outcomes like satisfaction and confidence generally require repeated exposure to achieve meaningful development [26]. Both groups in this study received the same theoretical instruction before the intervention, which could have equalized baseline attitudes toward learning and reduced the likelihood of post-intervention differences in affective perceptions. Although no study was located that exactly examines the equalizing effect of identical theoretical instruction on affective perceptions, prior research in educational psychology supports the notion that initial learner attitudes and prior instructional exposure can moderate subsequent attitude change [37, 38].

Limitations

This study has several limitations. First, the quasi-experimental design without full randomization limits causal inference, despite group allocation. Second, the single-institution setting may restrict generalizability to other academic or cultural contexts. Third, self-reported measures of satisfaction and self-confidence may be prone to bias and may not fully capture affective outcomes. Fourth, due to the nature of the intervention, it was not feasible to blind participants to group allocation, which may have introduced performance bias.

Conclusion

Simulation-based laboratory training proved to be an effective instructional approach for enhancing undergraduate nursing students’ clinical skill. The observed improvements in skill performance over time highlight the value of simulation in fostering readiness for real-world clinical practice. However, the limited impact on students’ satisfaction and self-confidence suggests that simulation alone may not fully address the affective dimensions of learning. To support holistic student development, simulation should be complemented with additional instructional strategies—such as guided reflection, mentorship, or structured feedback. Prioritizing the integration of simulation as a core component of practice-oriented nursing education is essential for preparing competent and adaptive future healthcare professionals.

This study highlights the importance of integrating simulation-based laboratory training into nursing education to strengthen students’ clinical competence and readiness for professional practice. By providing a safe, controlled environment to develop and refine clinical skills, simulation can enhance nurses’ competency, confidence, and preparedness in real-world settings. Beyond undergraduate education, simulation also offers significant value for continuing professional development. Integrating simulation into in-service and lifelong learning programs can help practicing nurses maintain and advance their clinical competence, confidence, and adaptability in dynamic healthcare settings.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

B.C.A., K.Y.Ç., S.G.G., M.T., H.E., and H.K.B. contributed to the conceptualization of the study. All authors participated in data curation. B.C.A., K.Y.Ç., E.K.E., İ.B.K., and G.Ç.Ö. performed the formal analysis. B.C.A. was responsible for visualization and wrote the original draft of the manuscript. K.Y.Ç. also contributed to funding acquisition. All authors reviewed and edited the manuscript and approved the final version.

Funding

This study was supported by the Scientific Research Projects Coordination Unit of Karadeniz Technical University under project number TAY_2023_10594.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to ethical/commercial restrictions but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Health Sciences Scientific Research Ethics Committee of Karadeniz Technical University (Approval Number: 2023-86/E-13562490-050.01.04-436200; Date: 25 October 2023). Written informed consent was obtained from all participants prior to data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.