Exploring the Quality of Feedback in Entrustable Professional Activity Narratives Across 24 Residency Training Programs
Bibliographic record
Abstract
Background Competency-based medical education (CBME) has been implemented in many residency training programs across Canada. A key component of CBME is documentation of frequent low-stakes workplace-based assessments to track trainee progression over time. Critically, the quality of narrative feedback is imperative for trainees to accumulate a body of evidence of their progress. Suboptimal narrative feedback will challenge accurate decision-making, such as promotion to the next stage of training. Objective To explore the quality of documented feedback provided on workplace-based assessments by examining and scoring narrative comments using a published quality scoring framework. Methods We employed a retrospective cohort secondary analysis of existing data using a sample of 25% of entrustable professional activity (EPA) observations from trainee portfolios from 24 programs in one institution in Canada from July 2019 to June 2020. Statistical analyses explore the variance of scores between programs (Kruskal-Wallis rank sum test) and potential associations between program size, CBME launch year, and medical versus surgical specialties (Spearman’s rho). Results Mean quality scores of 5681 narrative comments ranged from 2.0±1.2 to 3.4±1.4 out of 5 across programs. A significant and moderate difference in the quality of feedback across programs was identified (χ 2 =321.38, P <.001, ε2=0.06). Smaller programs and those with an earlier launch year performed better ( P <.001). No significant difference was found in quality score when comparing surgical/procedural and medical programs that transitioned to CBME in this institution ( P =.65). Conclusions This study illustrates the complexity of examining the quality of narrative comments provided to trainees through 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".