Beyond Frameworks: An Interpretive Description of Engaging in Debriefer Feedback Conversations
Bibliographic record
Abstract
INTRODUCTION: Effective simulation debriefing fosters reflective practice and enhanced learning outcomes. Although various debriefing frameworks and debriefer competency evaluation tools exist, less is known about the experience of giving and receiving debriefer feedback. The aims of this study were to explore simulation facilitators' perspectives of engaging in debriefer feedback conversations and to identify factors influencing the acceptance or rejection of the feedback. METHODS: A qualitative interpretive description approach, informed by action research, was used. Focus groups were conducted with 27 faculty participants to capture their experience of giving and receiving debriefer feedback. Thematic analysis was conducted to identify key patterns in faculty perceptions of the debriefer feedback conversations. RESULTS: The following 4 themes captured faculty perspectives of effective debriefer feedback: (1) establishing and maintaining a relational culture, (2) embracing a growth mindset, (3) creating a safe space for self-reflection, and (4) remaining objective and goal oriented. Faculty valued feedback for self-reflection and professional development, emphasizing the importance of trust, respect, and psychological safety. A strong relational culture, growth mindset, and safe environment enabled feedback givers and receivers to engage meaningfully with constructive feedback, lean into practical objectives, and remain future focused. CONCLUSIONS: Effective debriefer feedback extends beyond structured frameworks and competency tools; it also depends on relational culture and implementation processes. When situated within a supportive and collegial environment and delivered via an integrated approach that prioritizes relationships and a growth mindset, debriefer feedback can be a valuable strategy for 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 | 0.039 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".