Learning with our peers: peer-led versus instructor-led debriefing for simulated crises, a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Although peer-assisted learning is known to be effective for reciprocal learning in medical education, it has been understudied in simulation. We aimed to assess the effectiveness of peer-led compared to instructor-led debriefing for non-technical skill development in simulated crisis scenarios. METHODS: Sixty-one undergraduate medical students were randomized into the control group (instructor-led debriefing) or an intervention group (peer debriefer or peer debriefee group). After the pre-test simulation, the participants underwent two more simulation scenarios, each followed by a debriefing session. After the second debriefing session, the participants underwent an immediate post-test simulation on the same day and a retention post-test simulation two months later. Non-technical skills for the pre-test, immediate post-test, and retention tests were assessed by two blinded raters using the Ottawa Global Rating Scale (OGRS). RESULTS: The participants' non-technical skill performance significantly improved in all groups from the pre-test to the immediate post-test, with changes in the OGRS scores of 15.0 (95% CI [11.4, 18.7]) in the instructor-led group, 15.3 (11.5, 19.0) in the peer-debriefer group, and 17.6 (13.9, 21.4) in the peer-debriefee group. No significant differences in performance were found, after adjusting for the year of medical school training, among debriefing modalities (P = 0.147) or between the immediate post-test and retention test (P = 0.358). CONCLUSIONS: Peer-led debriefing was as effective as instructor-led debriefing at improving undergraduate medical students' non-technical skill performance in simulated crisis situations. Peer debriefers also improved their simulated clinical skills. The peer debriefing model is a feasible alternative to the traditional, costlier instructor model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".