Evaluating the Quality of Narrative Feedback for Entrustable Professional Activities in a Surgery Residency Program
Bibliographic record
Abstract
OBJECTIVE: To assess the quality of narrative feedback given to surgical residents during the first 5 years of competency-based medical education implementation. BACKGROUND: Competency-based medical education requires ongoing formative assessments and feedback on learners' performance. METHODS: We conducted a retrospective cross-sectional study using assessments of entrustable professional activities (EPAs) in the Surgical Foundations curriculum at Queen's University from 2017 to 2022. Two raters independently evaluated the quality of narrative feedback using the Quality of Assessment of Learning score (0-5). RESULTS: A total of 3900 EPA assessments were completed over 5 years. Of assessments, 57% (2229/3900) had narrative feedback documented with a mean Quality of Assessment of Learning score of 2.16 ± 1.49. Of these, 1614 (72.4%) provided evidence about the resident's performance, 951 (42.7%) provided suggestions for improvement, and 499/2229 (22.4%) connected suggestions to the evidence. There was no meaningful change in narrative feedback quality over time ( r = 0.067, P = 0.002). Variables associated with lower quality of narrative feedback include: attending role (2.04 ± 1.48) compared with the medical student (3.13 ± 1.12, P < 0.001) and clinical fellow (2.47 ± 1.54, P < 0.001), concordant specialties between the assessor and learner (2.06 ± 1.50 vs 2.21 ± 1.49, P = 0.025), completion of the assessment 1 month or more after the encounter versus 1 week (1.85 ± 1.48 vs 2.23 ± 1.49, P < 0.001), and resident entrusted versus not entrusted to perform the assessed EPA (2.13 ± 1.45 vs 2.35 ± 1.66; P = 0.008). The quality of narrative feedback was similar for assessments completed under direct and indirect observation (2.18 ± 1.47 vs 2.06 ± 1.54; P = 0.153). CONCLUSIONS: Just over half of the EPA assessments of surgery residents contained narrative feedback with overall fair quality. There was no meaningful change in the quality of feedback over 5 years. These findings prompt future research and 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 | 0.028 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".