Feedback Quality in Geriatric Medicine: Analyzing Entrustable Professional Activities in a Competency-Based Curriculum
Bibliographic record
Abstract
Background Competency-based medical education (CBME) aims to enhance the quality of medical training by providing timely, actionable feedback through entrustable professional activities (EPAs). However, variability in feedback quality remains a concern across residency programs. Methods We conducted a retrospective analysis of EPA feedback forms from a geriatric medicine program, comparing two distinct time periods: 2019–2020 and 2021–2022. This program averages eight residents per year with 30 full-time faculty members. The quality of feedback was assessed based on timeliness, task orientation, actionability, and polarity. Results 404 EPAs were initiated and completed in 2019–2020, with 69% (n=278) being timely, 89% (n=360) task oriented, 50% (n=203) very actionable, and 62% (n=250) corrective in polarity. 851 EPAs were initiated in 2021–2022 and 76% (n=645) were completed, with 64% (n=410) being timely, 78% (n=501) task oriented, 40% (n=259) very actionable, and 47% (n=305) corrective in polarity. Timely feedback was more likely to be task-oriented (χ2(1)=11.87, p<.001), actionable (χ2(2)=24.40, p<.001), and corrective (χ2(3)=22.80, p<.001) in the second timeframe. Compared to the second timeframe, EPAs completed in the first timeframe were more likely to be task oriented (χ2(1)=22.08, p<.001), actionable (χ2(2)=25.54, p<.001), and corrective in polarity (χ2(3)=25.89, p<.001). Conclusions Our study revealed lower quality feedback over time since implementing CBME at a geriatric medicine subspecialty training program. The root causes of the reduction in quality were not explored but are theorized to be multifactorial. Further investigation into the reasons for the reduction in feedback quality will help direct interventions to better sustain the quality of CBME implementation.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".