Effect of combined individual-collective debriefing of participants in interprofessional simulation courses on crisis resource management: a randomized controlled multicenter trial.
Bibliographic record
Abstract
OBJECTIVES: Interprofessional simulation (IPS) training is an effective way to learn crisis resource management. The type of debriefing used in IPS training may affect participants' performance and their level of psychological safety. We aimed to assess and compare performance after standard collective debriefing versus a combination of individual and collective debriefing ("combined" approach). MATERIAL AND METHODS: Randomized, controlled multicenter trial. IPS sessions were randomized to have either standard or combined debriefing. Each team's performance in the IPS session was assessed with the Team Emergency Assessment Measure. The participants assessed the debriefing quality with the Debriefing Assessment for Simulation in Healthcare. RESULTS: Forty IPS sessions were randomized, and 30 were analyzed, 15 using standard collective debriefing and 15 the combined individual-collective method. Teams' performance improved with both types of debriefing, based on pre-post testing (P<.01), and there were no significant differences in overall performance scores between the 2 types of debriefing (P=.64). However, the combined approach was associated with higher scores for leadership skills (P<.05) and psychological safety, and the participants' learning experience was better (P<.05). CONCLUSION: During IPS courses on crisis resource management, debriefing improves participants' performance, but similar overall results can be obtained with both debriefing methods. Combined debriefing might be more effective for improving participants' leadership skills and psychological safety and also provide a better learning experience.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".