High-Fidelity Simulation’s Impact on Clinical Reasoning and Patient Safety: A Scoping Review
Bibliographic record
Abstract
Background Simulation-based learning is a recognized teaching approach in nursing education. However, research on the interplay of simulation and clinical reasoning development is limited. It is assumed that the opportunity to develop skills and clinical reasoning through simulation will contribute to patient safety. Purpose This scoping review aims to summarize the literature on the impact of high-fidelity simulation (HFS) on students' clinical reasoning and patients' safety in undergraduate nursing education and to use that information to provide recommendations to nursing educators. Methods A scoping review of the literature published between 2011 and 2022 was conducted following the guidelines of the Joanna Briggs Institute Manual for Evidence Synthesis and a methodological framework for scoping studies. Results Of 155 studies identified, 21 were included in the final review. Based on these studies, HFS may affect patient safety by stimulating clinical reasoning in a safe environment for undergraduate nursing students. Exposure to HFS improves students' readiness and alleviates "transition shock" and anticipatory anxiety in the clinical setting. HFS cannot replace but may be used in conjunction with real-life nursing practice to support the development of clinical reasoning in students. Conclusion Simulation promotes active learning as students engage in lifelike scenarios. Repetitive practice of nursing skills in simulation contributes to students' dexterity, leading to improved safety, clinical performance, and translation of theory to practice. In addition, prebriefing and debriefing sessions after the HFS activity provide opportunities for exploration of factors influencing patient safety.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".