[Hi-Fi simulation: Teaching crisis resource management to surgery residents].
Bibliographic record
Abstract
BACKGROUND: High-fidelity (HiFi) simulation has shown its effectiveness for teaching crisis resource management (CRM) principles, and our institutional experience in this area is mainly with anesthesiology residents. We recently added to our postgraduate curriculum a new CRM course designed to cater to the specific needs of surgical residents. AIM: This short communication describes the experience of the University of Montreal Simulation Centre (Centre d'Apprentissage des Attitudes et Habiletés Cliniques CAAHC) regarding HiFi simulationbased CRM and communication skills teaching for surgical residents. METHODS: Thirty residents agreed to participate in a simulation course with pre-established scenarios and educational CRM objectives on a voluntary basis. RESULTS: When surveyed immediately after the activity, all residents agreed that the educational objectives were well defined (80% "strongly agree" and 20% "agree"). The survey also showed that the course was well accepted by all participants (96% "strongly agree" and 4% "agree"). CONCLUSION: Further trials with randomized groups and more reliable assessment tools are needed to validate our results. Still, integrating HiFi simulation based CRM learning in the surgical residency curriculum seems like an interesting step.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".