Exploring Faculty Approaches to Feedback in the Simulated Setting
Bibliographic record
Abstract
INTRODUCTION: Feedback in clinical education and after simulated experiences facilitates learning. Although evidence-based guidelines for feedback exist, faculty experience challenges in applying the guidelines. We set out to explore how faculty approach feedback and how these approaches align with current recommendations. METHODS: There is strong evidence for the following four components of feedback: feedback as a social interaction, tailoring content, providing specific descriptions of performance, and identifying actionable items. Faculty preceptors participated in feedback simulations followed by debriefing. The simulations were video recorded, transcribed, and analyzed qualitatively using template analysis to examine faculty approaches to feedback relative to evidence-informed recommendations. RESULTS: Recorded encounters involving 18 faculty and 11 facilitators yielded 111 videos. There was variability in the extent to which feedback approaches aligned with recommended practices. Faculty behaviors aligned with recommendations included a conversational approach, flexibly adapting feedback techniques to resident context, offering rich descriptions of observations with specific examples and concrete suggestions, achieving a shared understanding of strengths and gaps early on to allow sufficient time for problem-solving, and establishing a plan for ongoing development. Behaviors misaligned with guidelines included prioritizing the task of feedback over the relationship, lack of flexibility in techniques applied, using generic questions that did not explore residents' experiences, and ending with a vague plan for improvement. CONCLUSIONS: Faculty demonstrate variability in feedback skills in relation to recommended practices. Simulated feedback experiences may offer a safe environment for faculty to further develop the skills needed to help residents progress within competency-based medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".