Approaches to interpersonal conflict in simulation debriefings: A qualitative study
Bibliographic record
Abstract
CONTEXT: Conflict during simulation debriefing can interfere with learning when psychological safety is threatened. Debriefers often feel unprepared to address conflict between learners and the literature does not provide evidence-based guidance within the simulation setting. The purpose of this study was to describe debriefers' approach to mediating interpersonal conflict and explore when, why and how they adopt mediation strategies. METHODS: We performed a secondary analysis of qualitative data collected as part of a larger study examining simulation debriefers' approaches to debriefing scenarios with different learner characteristics. For this study, we applied thematic analysis to transcripts from simulated debriefings (n = 10) and the associated pre-simulation (n = 11) and post-simulation (n = 10) interviews that focused on interpersonal conflict between learners. RESULTS: Debriefers described struggling with mediating conflict and the importance of self-awareness. Specific mediation strategies included intervening, addressing power relations, reconciling unproductive differences, leveraging different perspectives, circumventing the conflict, and shifting beyond the conflict; each of these strategies encompassed a number of particular skills. Situations that triggered a mediation approach were related to psychological safety, emotional intensity, and opportunities for shared understanding and productive learning. Debriefers applied mediation strategies and skills in a flexible and creative way. CONCLUSIONS: The strategies we have described for mediating interpersonal conflict between learners in simulation debriefing align with notions of psychological safety and may be useful in guiding future professional development for simulation 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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".