Within-Team Debriefing Versus Instructor-Led Debriefing for Simulation-Based Education
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of an interprofessional within-team debriefing with that of an instructor-led debriefing on team performance during a simulated crisis. BACKGROUND: Although instructor-led simulation debriefing is considered the "gold standard" in team-based simulation education, cost and logistics are limiting factors for its implementation. Within-team debriefing, led by the individuals of the team itself rather than an external instructor, has the potential to address these limitations. METHODS: One hundred twenty subjects were grouped into 40 operating room teams consisting of 1 anesthesia trainee, 1 surgical trainee, and 1 staff circulating operating room nurse. All teams managed a simulated crisis scenario (pretest). Teams were then randomized to either a within-team debriefing group or an instructor-led debriefing group. In the within-team debriefing group, the teams reviewed the video of their scenario by themselves. The teams in the instructor-led debriefing group reviewed their scenario guided by a trained instructor. Immediately after debriefing, all teams managed a different intraoperative crisis scenario (posttest). All sessions were videotaped. Blinded expert examiners used the validated Team Emergency Assessment Measure scale to assess crisis resource management performance of all teams in random order. RESULT: Team performance significantly improved from pretest to posttest (P = 0.008) regardless of the type of debriefing. There was no significant difference in the degree of improvement between within-team debriefing and instructor-led debriefing (P = 0.52). CONCLUSIONS: Within-team debriefing results in measurable improvements in team performance in simulated crisis scenarios. This form of debriefing may be as effective as instructor-led team debriefing, which could improve resource utilization and feasibility of team-based simulation (NCT01067378).
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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 | 0.028 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".