Managing psychological safety in debriefings: a dynamic balancing act
Bibliographic record
Abstract
Debriefings should promote reflection and help learners make sense of events. Threats to psychological safety can undermine reflective learning conversations and may inhibit transfer of key lessons from simulated cases to the general patient care context. Therefore, effective debriefings require high degrees of psychological safety-the perception that it is safe to take interpersonal risks and that one will not be embarrassed, rejected or otherwise punished for speaking their mind, not knowing or asking questions. The role of introductions, learning contracts and prebriefing in establishing psychological safety is well described in the literature. How to maintain psychological safety, while also being able to identify and restore psychological safety during debriefings, is less well understood. This review has several aims. First, we provide a detailed definition of psychological safety and justify its importance for debriefings. Second, we recommend specific strategies debriefers can use throughout the debriefing to build and maintain psychological safety. We base these recommendations on a literature review and on our own experiences as simulation educators. Third, we examine how debriefers might actively address perceived breaches to restore psychological safety. Re-establishing psychological safety after temporary threats or breaches can seem particularly daunting. To demystify this process, we invoke the metaphor of a 'safe container' for learning; a space where learners can feel secure enough to work at the edge of expertise without threat of humiliation. We conclude with a discussion of limitations and implications, particularly with respect to faculty development.
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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 | 0.203 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".