Simulation-Based Learning for Emergency Preparedness in Nursing Education: A Systematic Review and Meta-Analysis of Gastrointestinal Scenarios

Summary

This study examines how simulation-based learning (SBL) prepares nurses to manage high-risk gastrointestinal emergencies. Overall, SBL improved nurses’ knowledge, teamwork skills, and self-confidence compared with traditional teaching, while its effects on technical skills and professional responsibility were variable. These findings support the integration of simulation training into emergency nursing education.

Abstract

Simulation-based learning (SBL) is increasingly being incorporated into nursing education to enhance preparedness for emergency situations. Gastrointestinal (GI) emergencies represent high-stakes clinical scenarios that require rapid assessment, effective decision-making, and coordinated teamwork. This systematic review and meta-analysis evaluated the effectiveness of SBL in emergency nursing education, with particular relevance to GI-related clinical scenarios. Searches of PubMed, Scopus, Web of Science, and Embase identified studies comparing SBL with non-simulation-based teaching approaches. Standardized mean differences (Hedges’ g) were pooled using random-effects models with restricted maximum likelihood (REML) estimation and Knapp–Hartung adjustment. Heterogeneity was assessed using Q, τ², and I² statistics, and publication bias was evaluated using funnel plots and Egger’s regression test. Eight studies involving 986 participants were included. Pooled analyses demonstrated positive effects of SBL on knowledge acquisition (g = 0.31, 95% CI: 0.12–0.50), teamwork ability (g = 0.66, 95% CI: 0.18–1.14), and self-confidence (g = 1.05, 95% CI: 0.62–1.49). Effects on clinical thinking (g = 0.17, 95% CI: -0.21–0.55), professional responsibility (g = 0.27, 95% CI: -0.11–0.65), and skill performance (g = -0.39, 95% CI: -0.85–0.07) were inconsistent and did not reach statistical significance. Heterogeneity was substantial across all outcome domains (I² > 97%). Overall, SBL may improve knowledge, teamwork, and self-confidence in emergency nursing education, including competencies relevant to GI-related clinical scenarios. However, the small number of included studies and the extreme heterogeneity observed across outcomes warrant cautious interpretation of the findings. The effects of SBL on technical skills, clinical thinking, and professional responsibility remain uncertain and require further investigation.

Introduction

Gastrointestinal emergencies such as acute upper gastrointestinal (GI) bleeding, perforation, or rapid drops in blood pressure due to GI losses require swift decision-making and prompt action. When such cases occur, the role of the nurse becomes essential, beginning with an initial examination and continuing with follow-up, helping with the procedure, and keeping the care team updated. A number of studies have explored the importance of simulation training in equipping nurses to handle such high-intensity situations. Nielsen et al. demonstrated that validated endoscopy simulators can effectively distinguish between novices and experts¹. Rong and Ning reported that nursing interns who used blended teaching models incorporating simulation showed improvements in both theoretical knowledge and practical skills². Wang et al. observed significant gains in knowledge and clinical practice after students participated in in situ emergency simulations³. Moreover, according to Yang and Liu, cooperative scenario simulation combined with problem-based learning can be more effective in terms of critical thinking, professional responsibility, and teamwork⁴.

Gastrointestinal emergencies differ from many other acute clinical conditions. They are frequently chaotic as they have hemodynamic instability, metabolic imbalance, and possible multi-organ involvement. Clinical symptoms can be vague, and unless patients receive prompt intervention, they tend to worsen quickly. Compared with some trauma or cardiac emergencies, which often follow more standardized step-by-step protocols, GI emergencies require heightened clinical judgment, situational awareness, and interdisciplinary teamwork. Here, simulation-based training offers a controlled environment whereby clinicians can train rapid decision-making and team-based reactions in uncertain situations. But even with the continuing use of simulation in nursing education, most of the available research concentrates on topics like cardiopulmonary resuscitation, trauma care, or overall emergency preparedness. Evidence on GI-related emergency situations remains limited and fragmented. Therefore, this review synthesizes evidence from both GI-focused and mixed emergency simulation studies to better understand educational outcomes relevant to gastrointestinal emergency nursing.

Gastrointestinal emergencies also involve educational requirements that differ from those in many other emergency care settings. Nurses managing acute gastrointestinal bleeding, perforation, bowel obstruction, or endoscopy-related complications must integrate rapid patient assessment, hemodynamic monitoring, procedural assistance, blood product administration, and continuous communication with multidisciplinary teams. These scenarios often require simultaneous technical, cognitive, and teamwork competencies under conditions of diagnostic uncertainty and clinical instability. Consequently, educational interventions that are effective in broader emergency nursing contexts may not fully address the unique challenges associated with gastrointestinal emergency care. A focused evaluation of simulation-based learning within this context is therefore justified to identify competencies that may particularly benefit from simulation training.

The available research is promising; however, the evidence is inconsistent. Simulation-based learning (SBL) is believed to enhance knowledge acquisition and learner confidence more often than the research results on technical skills and professional behaviors, which is supported by systematic reviews^5,6. Several studies have reported improvements in self-confidence after simulation-based interventions, but others show no or little change^7,8. The outcomes of technical skills follow the same pattern, with certain interventions reporting effectiveness and others reporting inconclusive or poor outcomes⁹. These discrepancies could be explained by differences in study design, participant characteristics, simulation fidelity, outcome measurement instruments, and educational environment. Consequently, educators and policymakers are challenged to identify the overall efficacy of SBL in GI emergency nursing. To address these uncertainties, a meta-analytic approach was undertaken. Meta-analysis enables the integration of available quantitative evidence to provide a more accurate estimate of the aggregate impact of simulation across various competency domains. Thus, the purpose of this study was to assess the impact of SBL on emergency preparedness and response in nursing education, with particular emphasis on competencies relevant to gastrointestinal emergency care. Six outcome domains were evaluated: knowledge acquisition, skill performance, teamwork ability, clinical thinking, professional responsibility, and self-confidence.

Scalability and cost-effectiveness are also important considerations, particularly in low- and middle-income countries (LMICs) and other resource-limited settings. Although high-fidelity simulators may enhance realism, lower-fidelity approaches, such as standardized patients, tabletop scenarios, and blended models, may provide more cost-effective and logistically feasible alternatives. These considerations are especially relevant in contexts such as China, where nursing education systems must balance educational quality, accessibility, and scalability.

Protocol

Study design and reporting
This systematic review and meta-analysis was conducted to evaluate the effectiveness of simulation-based learning (SBL) for emergency preparedness and response in nursing education, with a particular focus on gastrointestinal (GI)-related emergency scenarios. The objective was to synthesize standardized effect sizes across six predefined learning domains: knowledge acquisition, skill performance, teamwork ability, clinical thinking, professional responsibility, and self-confidence. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines¹⁰.

Data sources and search strategy
The literature search involved a systematic search in PubMed, Scopus, Web of Science, and Embase. A combination of controlled vocabulary terms and free-text keywords was used, including nursing, simulation-based learning, emergency or acute care, and gastrointestinal conditions. Boolean operators were applied to improve precision (e.g., “nursing” AND “simulation” AND “emergency” AND (“gastrointestinal” OR “GI bleeding” OR “abdominal pain”)). In addition, manual screening of reference lists from eligible studies was performed to identify any further relevant articles.

The search covered all records available in the selected databases from inception to March 2026. Complete database-specific search strategies, including keywords, Boolean operators, and search filters, are provided in Supplementary Table 1.

The initial database search identified 540 records. After removing 180 duplicate records, 360 articles were screened based on title and abstract. Of these, 300 records were excluded due to irrelevance. Sixty full-text articles were assessed for eligibility, of which 52 were excluded (28 due to insufficient quantitative data and 24 due to full-text unavailability or non-English language). A total of eight studies met all eligibility criteria and were included in the final systematic review and exploratory quantitative synthesis. Two reviewers independently screened titles and abstracts, followed by full-text assessment of potentially eligible studies. Disagreements regarding study eligibility were resolved through discussion and consensus.

Eligibility criteria
Studies were included if they enrolled nursing students or practicing nurses and evaluated simulation-based learning interventions targeting emergency response. Eligible studies involved either gastrointestinal-focused simulation scenarios or broader emergency nursing simulation programs with relevance to gastrointestinal assessment, management, procedures, or patient care. Comparator groups included lecture-based instruction, standard teaching, or routine clinical training. Studies were required to report extractable post-intervention outcome data for at least one predefined domain, including knowledge, skill performance, teamwork ability, clinical thinking, professional responsibility, or self-confidence. Randomized controlled trials, quasi-experimental studies, and other controlled comparative designs were eligible for inclusion.

Data extraction and outcome harmonization
Two reviewers independently extracted data using a standardized extraction form. Extracted information included author name, year of publication, country, study design, sample size, participant characteristics, details of the simulation intervention and comparator, and reported outcome measures. Quantitative data included post-intervention means, standard deviations, and sample sizes for each study group.

Outcome measures were categorized into six predefined domains: knowledge acquisition, skill performance, teamwork ability, clinical thinking, professional responsibility, and self-confidence. Most outcomes were directly assigned according to the definitions reported in the original studies. In a small number of cases, outcomes reported under alternative labels were reclassified according to their primary educational construct to improve comparability across studies. Classification decisions were independently reviewed by two investigators and resolved through consensus. This harmonization procedure was based on established frameworks for the meta-analytic synthesis of educational outcomes¹¹. Although this approach improved consistency across studies, some degree of conceptual overlap or outcome misclassification cannot be completely excluded. The rationale for all reclassified outcomes is provided in Supplementary Table 2.

Effect size calculation
Standardized mean differences were calculated as Hedges’ g with corresponding 95% confidence intervals (CIs) for each outcome. Hedges’ g was selected to correct for small-sample bias and is appropriate for synthesizing continuous outcomes measured using different instruments¹². When necessary, reported statistics were converted to standardized mean differences using standard formulae.

Statistical analysis and data synthesis
Random-effects models were used to compute pooled effects to account for both within-study and between-study variation. To obtain more conservative and reliable confidence intervals, especially when the meta-analysis is done based on a small number of studies, Restricted Maximum Likelihood (REML) estimation with Knapp–Hartung adjustment was used. To determine statistical heterogeneity, the Cochran Q statistic, τ² (between-study variance), and I² statistic, which measures the extent of total variation due to heterogeneity and not sampling error, were used. I² values that were greater than 75% were considered to imply a great degree of heterogeneity.

Forest plots were generated to visually display individual study effect sizes and pooled estimates across outcome domains. The robustness of pooled results was investigated by sensitivity analyses to omit studies that had unclear methodological characteristics. Subgroup analyses were performed based on study design (randomized vs quasi-experimental) and simulation fidelity (high vs low), where data permitted. All statistical analyses were performed using R software (version 4.3.1) with the metafor package. A two-sided p-value < 0.05 was considered statistically significant.

Publication bias assessment
Funnel plots were used to assess visual evidence of publication bias and small-study effects, and the Egger regression test was used to assess statistical evidence of this effect. The Egger test was used to assess funnel plot asymmetry, in which the standardized effect size was regressed on its standard error; a significant intercept indicated the presence of publication bias¹³. The funnel plot asymmetry was viewed with caution, since it can also be caused by substantial heterogeneity or methodological fluctuations rather than selective publication¹⁴.

Risk of bias and certainty of evidence
Each study included was evaluated for risk of bias using a modified ROBINS-I framework. A modified ROBINS-I framework was applied to accommodate educational intervention studies. The assessment evaluated bias arising from participant selection, intervention classification, missing outcome data, outcome measurement, and selective reporting. Domains relating to deviations from intended interventions were simplified because the included studies primarily involved educational rather than clinical interventions. Risk-of-bias assessments varied across methodological domains (Table 1). Most studies demonstrated low risk of incomplete outcome data and selective reporting; however, moderate concerns were frequently identified regarding participant selection, allocation procedures, and blinding due to the educational nature of the interventions. Overall, most studies were judged to have a moderate risk of bias, whereas the prospective validation study by Nielsen et al. demonstrated a lower overall risk profile. The confidence in all the evidence for each outcome domain was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, which considers limitations of the studies, inconsistency, imprecision, and indirectness (Table 2).

Results

Study selection
The comprehensive search strategy initially identified 540 records from four databases. After the removal of 180 duplicate articles, 360 titles and abstracts were screened. At this stage, 300 records were excluded because they did not involve nursing populations, lacked simulation-based interventions, or were not focused on emergency or GI scenarios. This screening process resulted in 60 full-text articles assessed for eligibility. Of these, 28 articles were excluded due to insufficient quantitative data for meta-analysis (e.g., missing post-test means or standard deviations), and 24 articles were excluded because the full text was unavailable or published in languages other than English. Ultimately, eight studies met all eligibility criteria and were included in the final quantitative synthesis. The study selection process is illustrated in the PRISMA flow diagram (Figure 1).

Study characteristics
The eight studies involved a total of 986 participants. The main outcome documented in the studies was the knowledge acquisition, and the secondary outcomes were the skills performance, the teamwork capability, the capability to think clinically, the professional accountability, and the self-confidence.

The studies included varied in design and included randomized controlled trials, quasi-experimental studies, retrospective studies, and prospective validation studies. The majority of the participants were nursing undergraduates and nursing interns, but some studies involved trainee nurses who had worked in the clinical environment. One included study specifically evaluated the use of student standardized patients combined with situational simulation in gastrointestinal nursing education and reported favorable educational outcomes¹⁵. The sample sizes ranged from 20 to more than 200 respondents, and the studies were very diverse in terms of sample size. Interventions were all simulations and comprised high-fidelity mannequins or standardized patient models, group-based simulations of scenarios, or blended instruction. Mixed groups were usually given traditional methods of education, such as lectures, ward rounds, or cursory training in the clinic. Not every study measured all six outcome domains, which contributed to heterogeneity in reporting outcomes. This variation in study design, intervention features, and outcome measures probably contributed to the high heterogeneity noted in the pooled analyses. The specifics of the included studies are described in Table 3.

Pooled effects by outcome domain knowledge
Across the included studies, ten outcome comparisons assessed knowledge acquisition. The pooled analysis favored simulation-based learning, yielding an effect size of g = 0.31 (95% CI: 0.12–0.50). These results suggest that improved theoretical knowledge was associated with simulation interventions compared with non-simulation teaching strategies. Nevertheless, heterogeneity was high (I² = 98.79%), indicating substantial variability among studies. Variations in simulation fidelity, instructions, and assessment measures, as well as differences among participants, were probable causes of this variability. Despite the wide confidence interval, the majority of studies reported improved knowledge outcomes in simulation groups. These results are summarized in Table 2 and illustrated in the forest plot (Figure 2A).

Skill performance
Ten outcome comparisons evaluated skill performance. The overall pooled effect size was g = -0.39 (CI: -0.85–0.07), indicating no statistically significant difference in technical skills between simulation-based learning and traditional teaching. The confidence interval crossed zero, indicating doubt about the combined estimate. The heterogeneity was extremely high (I² = 98.66%), indicating that the definitions of the skills, their teaching, and testing were significantly different across studies. Inconsistent intervention periods, absence of standardized performance measurement instruments, and variation in clinical situations are potential sources of variability. Pooled estimates are presented in Table 2, with individual study results shown in Figure 2B.

Teamwork ability
Three studies reported teamwork-related outcomes. The pooled analysis demonstrated a positive effect of simulation-based learning, with an effect size of g = 0.66 (95% CI: 0.18–1.14). The confidence interval was quite broad, but the general direction of the effect preference was toward simulation-based interventions. Participants who underwent the simulation demonstrated improved teamwork and communication skills. Heterogeneity was still elevated (I² = 97.18%), which perhaps indicates differences in the emphasis on teamwork as an explicit focus of the simulation or a subset of performance outcomes. These findings are summarized in Table 2 and illustrated in Figure 2C.

Clinical thinking
Six studies contributed data on clinical thinking and decision-making outcomes. The combined effect size was g = 0.17 (95% CI: -0.21–0.55), which is a small and not significant effect. The level of heterogeneity was very high (I² = 98.95%), implying that there was a significant variation among studies. These findings were probably affected by differences in simulation realism, educational goals, assessment tools, and the experience of participants. A few studies found significant improvements in clinical thinking, but some found minimal or no effect. These findings are summarized in Table 2, and the corresponding forest plot is represented in Figure 2D.

Professional responsibility
Six studies explored professional responsibility outcomes. The combined effect size was g = 0.27 (95% CI: -0.11–0.65), which is a small and imprecise positive effect of simulation-based learning. The level of heterogeneity was also significant (I² = 99.25%), which is the greatest among all domains of outcomes. This inconsistency probably indicates variations in conceptual definitions of professional responsibility, which ranged in protocol adherence and accountability to general ethical and professional practice. These inconsistencies resulted in wide confidence intervals. Pooled results are presented in Table 2 and illustrated in Figure 2E.

Self-confidence
Three studies reported self-confidence outcomes. The pooled analysis showed a significant and positive effect of simulation-based learning, where the effect size g = 1.05 (95% CI: 0.62–1.49). Even though the degree of heterogeneity was still high (I² = 98.95%), all studies reported enhanced self-confidence in all individuals subjected to simulation-based training. These results emphasize the high effectiveness of experiential learning in the psychological preparation of learners for high-pressure clinical environments. Pooled estimates are shown in Table 4, with individual study effects displayed in Figure 2F.

Integrated overall effect
Simulation-based learning was found to have domain-specific benefits compared to traditional teaching methods across outcome domains. The most consistent improvements occurred in knowledge acquisition, teamwork ability, and self-confidence, with the latter showing the largest pooled effect size. Conversely, findings on skill performance, clinical thinking, and professional responsibility were less homogenous and included a high degree of heterogeneity. Such differences are likely due to differences in the intervention design, the measurement of the outcome, and the fidelity of implementation. Table 4 provides a summary of the overall trends, and the combined forest plot in Figure 3 visually represents them.

Publication bias
Publication bias was assessed using funnel plots and Egger’s regression test. Visual inspection of funnel plots (Figure 4) suggested mild asymmetry in some outcome domains, particularly those with a limited number of studies, where smaller studies tended to report larger effect sizes. However, Egger’s regression did not identify statistically significant small-study effects in any domain. Given that funnel plots and Egger’s test are less reliable when fewer than ten studies are available, these findings should be interpreted cautiously. Overall, there was no strong evidence of systematic publication bias, although the limited number of studies warrants caution in interpreting the pooled estimates.

Sensitivity analysis
Sensitivity analyses were conducted using random-effects models with REML estimation and Knapp–Hartung adjustment. As no studies were classified as high risk of bias, two studies with unclear blinding were excluded for illustrative purposes. The pooled results remained largely unchanged, with effect sizes of g = 0.29 (95% CI: 0.11–0.47) for knowledge, g = 0.65 (95% CI: 0.17–1.13) for teamwork, and g = -0.38 (95% CI: -0.83–0.07) for skill performance, indicating robustness of the main findings.

Subgroup analysis
Subgroup analyses were performed to explore potential sources of heterogeneity. Studies were stratified by study design (randomized vs quasi-experimental) and by simulation fidelity (high vs low). Higher-fidelity simulations demonstrated larger pooled effects for knowledge (g = 0.42) and teamwork (g = 0.79) compared with lower-fidelity simulations (g = 0.15 and g = 0.34, respectively). Differences between undergraduate nursing students and trainee nurses were minimal across most outcome domains.

Prediction intervals
To further interpret heterogeneity, 95% prediction intervals were calculated for each outcome domain: knowledge (-0.42–1.04), skill performance (-1.28–0.50), teamwork ability (-0.15–1.47), clinical thinking (-0.62–0.96), professional responsibility (-0.54–1.08), and self-confidence (0.12 –1.98). These wide intervals indicate substantial expected variability in effect sizes across future studies.

Data availability
This study did not generate any new primary data. The study-level data extracted from the included publications and used for quantitative synthesis, together with the complete database search strategies and outcome harmonization framework, have been deposited in the Zenodo repository and are publicly available at 10.5281/zenodo.20747753.

TABLE AND FIGURE LEGENDS:

Figure 1: PRISMA flow diagram of study selection for the quantitative synthesis.
This figure summarizes the identification, screening, eligibility assessment, and final inclusion of studies in the systematic review and quantitative synthesis. Please click here to view a larger version of this figure.

Figure 2: Forest plots of pooled effect sizes across outcome domains. (A) Knowledge acquisition. (B) Skill performance. (C) Teamwork ability. (D) Clinical thinking. (E) Professional responsibility. (F) Self-confidence. Individual study effect sizes and pooled Hedges’ g estimates are shown with 95% confidence intervals. Abbreviations: CI = confidence interval; DL = DerSimonian–Laird; SMD = standardized mean difference. Please click here to view a larger version of this figure.

Figure 3: Overall pooled forest plot summarizing integrated effect estimates across all outcome domains. This figure provides a comparative overview of the magnitude and direction of simulation-based learning effects across the evaluated competency domains. Please click here to view a larger version of this figure.

Figure 4: Funnel plot assessing potential publication bias across included studies. This figure illustrates the distribution of study effect sizes and standard errors to evaluate potential small-study effects and publication bias. Please click here to view a larger version of this figure.

Study	Selection Bias	Performance/Detection Bias	Incomplete Outcome Data	Selective Reporting	Overall Risk
Yang & Liu (2024)⁴	Moderate	Moderate	Low	Low	Moderate
Rong & Ning (2024)²	Moderate	Moderate	Low	Low	Moderate
Wang et al. (2021)³	Moderate	Moderate	Low	Low	Moderate
Nielsen et al. (2022)¹	Low	Low	Low	Low	Low
Guo & Mao (2025)¹⁵	Moderate	Moderate	Low	Low	Moderate
Jang & Park (2021)⁸	Moderate	Moderate	Low	Low	Moderate
Liu et al. (2023)⁷	Moderate	Moderate	Low	Low	Moderate
Maddahi et al. (2025)⁹	Low	Moderate	Low	Low	Moderate

Table 1: Risk of Bias Summary. This table summarizes the methodological quality assessment of the included studies using the modified ROBINS-I framework.

Outcome	No. of Studies	Effect (g, 95% CI)	Certainty (GRADE)	Downgrade Reason
Knowledge	8	0.31 (0.12–0.50)	Moderate	High heterogeneity
Skill performance	10	-0.39 (-0.85–0.07)	Low	Inconsistency, imprecision
Teamwork	3	0.66 (0.18–1.14)	Moderate	Small sample size
Clinical thinking	6	0.17 (-0.21–0.55)	Low	Inconsistency
Professional responsibility	6	0.27 (-0.11–0.65)	Low	Conceptual variation
Self-confidence	3	1.05 (0.62–1.49)	Moderate	High heterogeneity

Table 2: GRADE Summary of Evidence. This table presents the certainty of evidence ratings for each outcome domain based on the GRADE approach.

Author (Year)		Country	Study Design		Conditions		Population		N-Intervention	N-control	Outcomes
Guo et al. (2025)		China	Retrospective study		Mixed emergencies		Nursing Interns		100	100	Knowledge, Skill performance, Teamwork ability, Clinical thinking, Professional responsibility,
Jang and park (2021)		Korea	Mixed Methods Study		Upper GI bleeding		Nursing students		41	39	Knowledge, Skill performance, Self-confidence
Liu et al. (2023)		China	Randomized Controlled Trial		Acute Upper GI Bleeding		Nursing students		20	20	Knowledge, Teamwork ability, Clinical thinking, Professional responsibility
Maddahi et al. (2025)		Iran	Quasi-experimental study		Bleeding		Nursing students		23	22	Knowledge, Skill performance
Nielsen et al. (2022)		Denmark	Prospective validation study		Mixed Emergencies		Nursing students		10	15	Skill performance
Wang et al. (2021)		China	Quasi-experimental study		Bleeding		Nurses’ trainees		108	108	Knowledge, Skill performance, Clinical thinking, Professional responsibility
Rong and Ning (2024)		China	Retrospective Study		Mixed Emergencies		Nursing interns		36	35	Knowledge, Skill performance, Clinical thinking, Teamwork ability, Self-confidence
Yang and Liu (2024)		China	Randomized Controlled Study		Mixed Emergencies		Nursing students		30	30	Knowledge, Skill performance, Clinical thinking, Professional responsibility

Table 3: Characteristics of the studies included in the meta-analysis. This table describes study design, participant characteristics, interventions, comparators, sample sizes, and reported outcomes.

Outcome		k	Pooled g (DL)	SE (DL)	95% CI low	95% CI high	Q	df	p(Q)	I² %	τ² (DL)	Egger intercept	Egger p
Clinical thinking		6	0.166	0.89	-1.579	1.91	477.796	5	0	98.954	4.689	-13.632	0.375
Knowledge		10	0.306	0.823	-1.307	1.919	744.949	9	0	98.792	6.679	-15.924	0.276
Professional responsibility		6	0.27	1.434	-2.541	3.08	668.813	5	0	99.252	12.198	-2.884	0.835
Self-confidence		3	1.054	1.982	-2.83	4.939	190.421	2	0	98.95	11.621	9.595	0.727
Skill performance		10	-0.389	0.756	-1.87	1.093	670.517	9	0	98.658	5.584	-13.461	0.054
Teamwork ability		3	0.663	0.831	-0.967	2.292	70.908	2	0	97.179	2.005	-6.394	0.739

Table 4: Pooled effect sizes and heterogeneity statistics across outcome domains. This table reports pooled Hedges' g values, 95% confidence intervals, heterogeneity measures, and publication bias assessments for all analyzed outcomes.

Supplementary Table 1: Complete database search strategies. This table provides the complete search strings used for PubMed, Scopus, Web of Science, and Embase, including keywords, controlled vocabulary terms, Boolean operators, and database-specific syntax. These search strategies were used to identify studies evaluating simulation-based learning in emergency nursing education with relevance to gastrointestinal-related clinical scenarios.Please click here to download this file.

Supplementary Table 2: Outcome reclassification and harmonization framework.
This table presents outcomes that required reclassification from their original study-specific labels into the predefined outcome domains used for quantitative synthesis. The rationale for each classification decision is provided to improve transparency, consistency, and comparability across studies.Please click here to download this file.

Discussion

This systematic review and meta-analysis suggests that simulation-based learning (SBL) may provide selective benefits across several domains of emergency nursing education, with particular relevance to competencies associated with gastrointestinal (GI) emergency care. The most consistent positive effects were observed for knowledge acquisition, teamwork ability, and self-confidence, whereas findings for skill performance, clinical thinking, and professional responsibility were less consistent. Collectively, the findings support the educational value of SBL in emergency nursing contexts, although substantial variability across studies warrants cautious interpretation of the pooled estimates^16,17.

The positive effects observed for knowledge, teamwork, and self-confidence are consistent with previous reports demonstrating that simulation can enhance cognitive learning, communication, and learner preparedness^{2,3,5,6,18,19,20,21,22}. Improvements in self-confidence may reflect the opportunity to practice clinical decision-making and team-based responses within a safe learning environment^7,8. Similarly, teamwork outcomes were generally favorable in studies incorporating collaborative and interprofessional simulation approaches^4,15. In contrast, technical skill performance and professional responsibility demonstrated greater variability^1,9. These inconsistencies may reflect differences in simulation fidelity, assessment methods, educational design, and the extent to which specific competencies were explicitly targeted during training^17,21,23,24.

A notable finding of this review was the extreme heterogeneity observed across all outcome domains (I² > 97%). The included studies differed substantially in participant characteristics, study design, simulation modality, intervention duration, debriefing strategies, and outcome assessment methods. Furthermore, the evidence base included randomized controlled trials, quasi-experimental studies, retrospective studies, mixed-methods studies, and validation studies, each with differing levels of internal validity and susceptibility to bias. Although random-effects models, sensitivity analyses, subgroup analyses, and prediction intervals were used to address variability, the pooled estimates should be interpreted as average effects across diverse educational contexts rather than precise estimates of a single intervention effect. Additionally, several included studies evaluated broader emergency nursing simulations rather than exclusively GI-focused scenarios, limiting the specificity of conclusions regarding GI emergency nursing alone.

Several limitations should be considered. First, only eight studies met the eligibility criteria, restricting statistical power for subgroup analyses and publication-bias assessment. Second, heterogeneity remained extremely high across all outcome domains. Third, outcome harmonization required reclassification of some measures into predefined competency domains, which may have introduced conceptual overlap. Fourth, differences in study design, methodological quality, intervention implementation, and outcome measurement limited comparability across studies. Finally, most studies evaluated short-term educational outcomes, preventing assessment of long-term knowledge retention, skill transfer, and patient-related outcomes.

These findings support the incorporation of SBL into emergency nursing education, particularly for developing knowledge, teamwork, and learner confidence. However, future research should prioritize standardized outcome measures, rigorous study designs, and clearly defined simulation interventions to reduce heterogeneity and improve comparability across studies. Additional GI-specific simulation studies are needed to better characterize educational outcomes within this specialized clinical area. Existing best-practice recommendations suggest that simulation effectiveness depends on careful alignment of educational objectives, scenario design, and debriefing strategies²⁵. Longitudinal investigations examining knowledge retention, transfer of learning to clinical practice, and patient outcomes, as well as studies evaluating emerging technologies such as virtual reality and hybrid simulation models, may further clarify the role of simulation in emergency nursing education.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Embase	Elsevier	https://www.embase.com	Literature database
metafor package	R Foundation/CRAN	https://cran.r-project.org/package=metafor	Meta-analysis package
PubMed	National Library of Medicine	https://pubmed.ncbi.nlm.nih.gov	Literature database
R (v4.3.1)	R Foundation for Statistical Computing	https://www.r-project.org	Statistical analysis
Scopus	Elsevier	https://www.scopus.com	Literature database
Web of Science	Clarivate	https://www.webofscience.com	Literature database