Implementation of Entrustable Professional Activities assessments in a Canadian obstetrics and gynecology residency program: a mixed methods study
Bibliographic record
Abstract
Background: Since the implementation of competency-based medical education (CBME) across residency training programs in Canada, there has been limited research understanding how entrustable professional activity (EPA) assessments are used by faculty supervisors and residents. Objective: This study examines how EPA assessments are used in an Obstetrics and Gynecology residency program and the impact of implementation on both groups. Methods: A mixed methods study design was used. Part one involved the aggregation of descriptive data of EPA assessment completion for postgraduate year 1 and 2 residents from July 2019 to May 2020. Part two involved a thematic analysis of semi-structured interviews of residents and faculty. Results: There was significant uptake of EPA assessments across community and teaching hospitals with widespread contribution of assessment data from faculty. However, both residents and faculty reported that the intended design of EPA assessments as low-stakes assessments to provide formative feedback is not how EPA assessments are experienced. Residents and faculty noted the increased level of administrative burden and related perceived stress amongst the resident group. Conclusions: The implementation of EPA assessments is feasible across a variety of sites. However, previous measurement challenges remain. Neither residents nor faculty perceive the value of EPAs to improve feedback, despite their intended nature.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".