Psychiatric Education and Simulation: A Review of the Literature
Bibliographic record
Abstract
OBJECTIVES: Simulation methodologies are integral to health professional education at all levels of training and across all disciplines. This article reviews the literature on simulation in psychiatric education and explores recent innovations and emerging ethical considerations related to teaching and evaluation. METHOD: The authors searched the MEDLINE, ERIC, and PsycINFO databases from 1986 to 2006 using multiple search terms. A detailed manual search was conducted of Academic Psychiatry, Academic Medicine, and Medical Education. Literature indirectly relevant to the search parameter was also included. RESULTS: Of the more than 5000 articles retrieved from the literature on simulation and health professional education, 72 articles and books used the terms simulation and standardized patients or role play and psychiatry education. Of the more than 900 articles on objective structured clinical examinations (OSCE), 24 articles related specifically to psychiatry OSCEs. CONCLUSIONS: Live simulation is used in teaching, assessment, and research at all levels of training in psychiatric education. Simulated and standardized patients are useful and appropriate for teaching and assessment and are well accepted at both undergraduate and post-graduate level. There is also an important place for role play. Further research is needed regarding the implications of different simulation technologies in psychiatry.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".