Evaluating facilitator adherence to a newly adopted simulation debriefing framework
Bibliographic record
Abstract
Background: Post-simulation debriefing is a critical component of the learning process for simulation-based medical education, and multiple frameworks have been established in an attempt to maximize learning during debriefing through guided reflection. This study developed and applied a rubric to measure facilitator adherence to the newly adopted Promoting Excellence and Reflective Learning in Simulation (PEARLS) debriefing framework to evaluate the efficacy of current faculty development. Methods: A retrospective review of 187 videos using a structured 13-behavior rubric based on the PEARLS debriefing model was conducted of facilitator-learner debriefings following a simulated clinical encounter for medical students. The aggregate results were used to describe common patterns of debriefing and focus future faculty development efforts. Results: In total, 187 debriefings facilitated by 32 different facilitators were analyzed. Average scores for each of the 13 PEARLS framework behaviors ranged from 0.04 to 0.971. Seven items had an average of ≥ 0.77, ten averaged > 0.60 and two averaged < 0.20. Conclusions: Faculty adhered to some behaviors elicited by the PEARLS model more consistently than others. These results suggest that faculty facilitators are more likely to adhere to frameworks that focus on educational behaviors and less likely to adhere to organizational or methodological frameworks.
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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 | 0.098 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".