Debriefing for Simulation-Based Medical Education: A Survey From the International Network of Simulation-Based Pediatric Innovation, Research and Education.
Bibliographic record
Abstract
CONTEXT: Current debriefing approaches and faculty development strategies for simulation educators differ around the world. We aim to describe the status of current debriefing practice and faculty development for simulation educators in this study. METHODS: We distributed a paper-based survey during 2 international conferences to obtain data from active International Network for Simulation-based Pediatric Innovation, Research and Education members. The survey was tested to ensure content validity and consisted of the following 3 constructs: demographic characteristics, current debriefing practice, and issues related to faculty development. RESULTS: One hundred nine of 114 participants (96%) completed the survey. Debriefing practice differs in terms of timing, duration, framework, and conversational framework. Most debriefings were less than 30 minutes (93/109, 85%), with many educators not using objective data during debriefing (47/109, 43%). Three- or 4-phase debriefing frameworks were used most commonly (66/109, 61%). Most participants have access to some faculty development opportunities (99/109, 91%). Barriers to faculty development are related to time and resource constraints (eg, freeing up facilitator's time: 75/109, 69%, competing priorities 64/109, 59%). Most participants indicated that their needs for debriefing to improve learning outcomes were met (95/109, 87%). The desired content for future faculty development opportunities varies between educators with different levels of expertise. CONCLUSIONS: Approaches to debriefing among members of an international pediatric simulation network vary considerably. Although faculty development opportunities were available to most participants, future simulation programs should work on addressing barriers and optimizing faculty development plans to meet the needs of their educators.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".