Simulation Education Approaches to Enhance Collaborative Healthcare: A Best Practices Review
Bibliographic record
Abstract
Interprofessional simulation can provide health profession program educators with an effective means to prepare future practitioners to engage in meaningful collaboration. This systematic review was conducted to identify best practice recommendations to enhance collaborative healthcare using interprofessional simulation education innovations for learners in pre-licensure nursing programs. Using a systematic review methodology, 375 articles were reviewed and 17 studies met the inclusion criteria. Based on the methodological strength of the research and the impact of the simulation innovations, the following simulation techniques were recommended: high-fidelity human patient simulators, role play, and didactic lecture and audience response didactic lecture, both followed by role play with a standardized patient. Instructor modeling was related to achievement of interprofessional competencies when compared to no modeling. Future research is needed to identify optimal timing for implementing interprofessional education innovations, for development of appropriate evaluation tools, and to determine the effects of collaborative practice on patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".