Comparing Entrustable Professional Activity Scores Given by Faculty Physicians and Senior Trainees to First-Year Residents
Bibliographic record
Abstract
Introduction Competency by Design (CBD) began on July 1, 2019, for postgraduate year 1 (PGY1) Canadian Core Internal Medicine (CIM) residents. Many entrustable professional activity (EPA) observations allow for assessment by either a faculty physician, senior medicine resident (SMR), or subspecialty resident (SSR). However, few studies exist that compare EPA scores and comments given by faculty vs senior trainees (SMRs and SSRs). This study aimed to identify differences in EPA scores and comments given to PGY1 residents by faculty physicians vs senior trainees. Methods Scores and comments of EPAs completed between July 1, 2019, and June 30, 2020, for 35 CIM PGY1 residents were extracted anonymously from the University of Alberta CBD platform. Scores from faculty vs senior trainees were compared with the Mann-Whitney U test and the Kruskal-Wallis test. Word counts for positive and constructive comments written by faculty vs senior trainees were compared with the independent t-test and one-way ANOVA. The most common two-word phrases in comments were identified with QI Macros software (Denver, CO: KnowWare International, Inc.). Results A total of 2226 EPAs were observed. Faculty physicians gave significantly lower EPA scores overall compared to senior trainees (U = 501706, P <0.001). Constructive comments written by faculty (M = 14.06, SD = 16.84) had lower word counts compared to senior trainees (M = 15.85, SD = 16.43) for overall EPAs (t{2224} = -2.528, P = 0.012). Conclusion Faculty physicians gave lower EPA scores and had lower word counts on constructive comments, compared to senior trainees. These results may help the ongoing implementation of Competence by Design.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".