The impact of clinical simulation on bridging the theory–practice gap in nursing education: a systematic review
Bibliographic record
Abstract
BACKGROUND: Bridging the gap between theoretical instruction and practical competence remains a central challenge in nursing education. Nursing students often struggle to transfer classroom-acquired knowledge into real-world clinical environments, resulting in decreased confidence, impaired decision-making, and compromised patient care. Clinical simulation has emerged as a promising pedagogical tool to address this longstanding theory-practice gap by recreating realistic scenarios in a controlled setting. METHOD: This systematic review synthesized studies published from 2010 to 2025 concerning the use of clinical simulation in nursing education. Five databases were searched using defined keywords to identify relevant studies. Eligible studies focused on simulation-based interventions aimed at enhancing nursing students' clinical competence, decision-making, confidence, and knowledge transfer. Data extraction was independently performed by reviewers, and methodological quality was assessed using the Cochrane RoB 2, CASP, and Newcastle-Ottawa Scale (NOS) checklists. A thematic synthesis approach was employed to analyze both qualitative and quantitative findings. RESULTS: Of the fifteen included studies, twelve reported significant improvements in nursing students' clinical decision-making, judgment, or self-confidence, involving over 1,100 participants. High-fidelity simulation and structured scenario-based interventions were particularly effective in enhancing core competencies such as cardiopulmonary resuscitation (CPR), infection control, and diagnostic reasoning. Thematic synthesis categorized findings into six domains: clinical decision-making, clinical judgment, learner self-confidence, empathy development, experiential learning, and learner satisfaction. Reported challenges included limited technological access, inconsistent debriefing, and insufficient faculty training. No adverse outcomes were noted, although potential publication bias and short follow-up durations were identified as limitations. CONCLUSION: Simulation-based education can serve as an effective and scalable strategy to reduce the theory-practice gap in nursing education. Its success depends on sustained implementation, institutional support, and pedagogical integration. Future research should emphasize long-term effectiveness and explore context-specific barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".