Co-debriefing for Simulation-based Education
Bibliographic record
Abstract
STATEMENT: As part of simulation-based education, postevent debriefing provides an opportunity for learners to critically reflect on the simulated experience, with the goal of identifying areas in need of reinforcement and correcting areas in need of improvement. The art of debriefing is made more challenging when 2 or more educators must facilitate a debriefing together (ie, co-debriefing) in an organized and coordinated fashion that ultimately enhances learning. As the momentum for incorporating simulation-based health care education continues to grow, the need for faculty development in the area of co-debriefing has become essential. In this article, we provide a practical toolbox for co-facilitators by discussing the advantages of co-debriefing, describing some of the challenges associated with co-debriefing, and offering practical approaches and strategies to overcome the most common challenges associated with co-debriefing in the context of simulation-based health care education.
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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 | 0.043 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".