Motivations for Entrustable Professional Activity Assessment: Gaps Between Curriculum Theory and Resident Reality
Bibliographic record
Abstract
Background Previous research demonstrates mixed reactions from residents toward competency-based medical education (CBME), and entrustable professional activities (EPAs) specifically. However, understanding what motivates residents to obtain EPAs may be vital to the longevity of CBME, given the emphasis on assessment for learning under this paradigm. Objective This study explored resident perspectives across 3 domains: motivation for obtaining EPAs, perceived importance of EPAs, and overall thoughts on CBME curriculum. Methods This was a sequential exploratory mixed-methods study involving 2 phases of data collection. Phase 1 was semi-structured interviews with residents enrolled in CBME at one Canadian institution from November 2019 to July 2020. Analyses included thematic and manifest content analysis. Phase 2 was an electronic close-ended survey to capture residents’ primary motivation for requesting EPAs and importance of EPAs for learning. Survey data were analyzed descriptively. Results Of 120 eligible residents, 25 (21%) and 107 (89%) participated in the interview and survey, respectively. Program requirement was the dominant motivation for obtaining EPAs. There was variability in perceived importance of EPAs on learning. Increased resident workload, gaming the system to maximize EPA scores, and lack of shared ownership from preceptors were cited as critiques of the curriculum. Survey responses corroborated interview findings. Conclusions Although many residents recognize the value of EPAs, the majority are not intrinsically motivated to seek out assessment under the current CBME framework.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".