Feedback that Lands: Exploring How Residents Receive and Judge Feedback During Entrustable Professional Activities
Bibliographic record
Abstract
Introduction: Receiving feedback from different types of assessors (e.g., senior residents, staff supervisors) may impact trainees' perceptions of the quantity and quality of data during entrustable professional activity (EPA) assessments. We evaluated the quality of EPA feedback provided by different assessors (senior residents, chief medical residents/subspecialty residents, and staff) and explored residents' judgements of the value of this feedback. Methods: From a database of 2228 EPAs, we calculated the frequency of contribution from three assessor groups. We appraised the quality of 60 procedure-related EPAs completed between July 2019 and March 2020 using a modified Completed Clinical Evaluation Report Rating (CCERR) tool. Next, we asked 15 internal medicine residents to sort randomly selected EPAs according to their judgements of value, as an elicitation exercise before a semi-structured interview. Interviews explored participants' perceptions of quality of written feedback and helpful assessors. Results: Residents completed over 60% of EPA assessments. We found no difference in modified-CCERR scores between the three groups. When judging EPA feedback value, residents described a process of weighted deliberation, considering perceived assessor characteristics (e.g., credibility, experience with EPA system), actionable written comments, and their own self-assessment. Discussion: Like other recent studies, we found that residents contributed most to procedure-related EPA assessments. To the established list of factors influencing residents' judgements of feedback value, we add assessors' adherence to, and their shared experiences of being assessed within, EPA assessment systems. We focus on the implications for how assessors and leaders can build credibility in themselves and in the practices of EPA assessments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".