“Debriefing-on-Demand”
Bibliographic record
Abstract
INTRODUCTION: Simulation is an effective tool in medical education with debriefing as the cardinal educational component. Alternate debriefing strategies might further enhance the educational value of simulation. Here, we pilot a novel strategy that allows trainees to initiate debriefing at any point during the scenario, when they consider it necessary. METHODS: With ethics approval, 8 postgraduate year 1 anesthesia residents (with no previous exposure to high-fidelity simulation) were randomly assigned to lead 2 of 8 scenarios with 2 debriefing strategies. With "debriefing-on-demand," residents had the option to initiate debriefing at any point in the scenario by activation of a "pause button"-in addition to undergoing conventional debriefing at the end of the scenario. Those randomized to "conventional debriefing" were debriefed only at the end of the scenario. All were allocated as team leader with both debriefing strategies and as a participant in remaining scenarios. Residents provided feedback regarding each method using Likert scales and completion of open-ended statements. RESULTS: Debriefing-on-demand was easily integrated into all scenarios, and most learners (88%) supported its use in future simulation sessions. The following 4 themes emerged from qualitative analyses: (1) improvements in the clarification and integration of knowledge, (2) reductions in stress/anxiety, (3) facilitated reflection on action, and (4) maintained realism comparable with conventional debriefing. CONCLUSIONS: Debriefing-on-demand was easily integrated into all scenarios and well received by these trainees new to simulation. Larger trials that use validated tools are needed to determine the absolute impact of debriefing-on-demand on stress levels and the overall learning value of simulation for trainees at different levels of training.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".