Scenario-Based Simulation in Medical and Nursing Education (2019–2025): Research Progress and Practice Insights
Bibliographic record
Abstract
With the transformation of medical education towards “competence-oriented” development, traditional theoretical teaching can hardly meet the clinical demand for practical talents. As a core means to connect “institutional education” with “clinical practice”, the scenario-based simulation teaching method has been increasingly applied in the fields of medical and nursing education. This teaching method takes simulated real clinical scenarios as the carrier and guides learners to engage in immersive role-playing and problem-solving, thereby realizing the in-depth integration of theoretical knowledge and practical skills. It can significantly improve learners’ clinical emergency response ability, humanistic communication ability, and teaching satisfaction. This article reviews the current application status of the scenario-based simulation teaching method in medical and nursing education, including its core application models, implementation effects, and existing problems. Furthermore, it proposes future development directions based on emerging practices such as the BOPPPS model and the Calgary-Cambridge communication model, aiming to provide references for medical and nursing educators to optimize teaching practices.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".