Competency-Based Cardiology Training: A Simple Approach to Improve Supervisor Completion of Entrustable Professional Activities
Bibliographic record
Abstract
Background Adult cardiology residency programs formally transitioned to Competency by Design (CBD) in July 2021. CBD was designed to establish clear learning expectations and increase opportunities for coaching; however, cited challenges include inconsistent participation by staff, and variable timelines for receiving feedback. This project was designed to implement a simple intervention to improve expiry rates and completion timelines of entrustable professional activities (EPAs). Methods EPAs triggered by cardiology residents at Dalhousie University between July 1, 2020 and February 28, 2023 were reviewed. The intervention consisted of performance reviews, including a grand rounds presentation, along with a personalized data set distributed to each staff supervisor, with individual statistics compared to group averages. Outcomes include EPAs completed per resident-months, time to completion, and percentage of expired EPAs. Results At 12 months postintervention, the percentage of expired EPAs decreased from 35.0% to 21.5% (odds ratio 0.51, CI 0.33–0.79; P = 0.03), and the time to completion decreased from 7.3 ± 5.99 days to 5.0 ± 5.78 days (difference –2.31, CI –3.55 to –1.07; P < 0.001). The number of EPAs completed per resident-months increased from 3.10 to 4.29 (rate difference 1.18; CI 0.64–1.72; P < 0.001), and the percentage of EPAs completed within the target time of 48 hours increased from 54.4% to 71.5% (OR 2.11, CI 1.27–3.50; P = 0.004). Conclusions Performance reviews in the form of a group presentation, along with the distribution of personalized data sets to supervisors, positively impacted EPA expiry rates, completion timelines, and completion rates, which helped facilitate the transition to CBD.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".