Coaching the Debriefer
Bibliographic record
Abstract
STATEMENT: Formal faculty development programs for simulation educators are costly and time-consuming. Peer coaching integrated into the teaching flow can enhance an educator's debriefing skills. We provide a practical guide for the who, what, when, where, why, and how of peer coaching for debriefing in simulation-based education. Peer coaching offers advantages such as psychological safety and team building, and it can benefit both the educator who is receiving feedback and the coach who is providing it. A feedback form for effective peer coaching includes the following: (1) psychological safety, (2) framework, (3) method/strategy, (4) content, (5) learner centeredness, (6) co-facilitation, (7) time management, (8) difficult situations, (9) debriefing adjuncts, and (10) individual style and experience. Institutional backing of peer coaching programs can facilitate implementation and sustainability. Program leaders should communicate the need and benefits, establish program goals, and provide assessment tools, training, structure, and evaluation to optimize chances of success.
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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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".