Looking in the mirror: Self-debriefing versus instructor debriefing for simulated crises*
Bibliographic record
Abstract
OBJECTIVE: To examine the effectiveness of self-debriefing as compared to instructor debriefing in the change of nontechnical skills performance of anesthesiology residents. DESIGN: Prospective, randomized, controlled study. SETTING: A university hospital simulation center. SUBJECTS: : Fifty anesthesiology residents. INTERVENTIONS: Subjects were instructed in the principles of nontechnical skills for crisis management. Subsequently, each resident participated in a high-fidelity simulated anesthesia crisis scenario (pretest). Participants were randomized to either a video-assisted self-debriefing or instructor debriefing. In the self-debriefing group, subjects reviewed their pretest scenario by themselves, guided by the Anesthetists' Non-Technical Skills scale. The instructor debriefing group reviewed their pretest scenario guided by an expert instructor also using the Anesthetists' Non-Technical Skills scale as a framework. Immediately following their respective debriefings, subjects managed a second simulated crisis (post-test). MEASUREMENTS AND MAIN RESULTS: After all data were collected, two blinded experts independently rated videos of all performances in a random order using the Anesthetists' Non-Technical Skills scale. Performance significantly improved from pretest to post-test (p < .01) regardless of the type of debriefing received. There was no significant difference in the degree of improvement between self-debriefing and instructor debriefing (p = .58). CONCLUSIONS: Nontechnical skills for crisis resource management improved with training, as measured by the Anesthetists' Non-Technical Skills scale. Crisis resource management can be taught, with measurable improvements. Effective teaching of nontechnical skills can be achieved through formative self-assessment even when instructors are not available.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".