Tensions in Competence by Design implementation: A qualitative multidisciplinary study to identify factors that promote and impede Entrustable Professional Activity acquisition among faculty and residents at a Canadian university
Bibliographic record
Abstract
Purpose. The purpose of this qualitative study was to explore residents’ and faculty’s experiences with EPAs to identify barriers and facilitators to EPA acquisition. This supported the subsequent development of recommendations for residents and faculty, residency program leadership, and institution to ensure that residents’ training and Competence Committee promotional decisions are optimized. Method. The authors conducted a qualitative study utilizing grounded theory and quality improvement research methods with residents and faculty from various specialties at a Canadian University. Through the utilization of a process mapping exercise, an interactive fishbone diagram exercise, and semi-structured focus groups, barriers and facilitators to EPA acquisition were identified and then thematically analyzed using a constant comparative technique, thereby generating a final codebook of results. Results. Six residents and six faculty from eight specialties participated. Two resident focus groups and one individual resident interview generated 2 hours and 36 minutes of audio-recorded data and 43 pages of transcribed data. Two faculty focus groups generated 1 hour and 44 minutes of audio-recorded data and 33 pages of transcribed data. Four primary categories for EPA barriers and facilitators were pre-defined according to the fishbone diagram. Fifteen themes of barriers and fifteen themes of facilitators, with varying degrees of subthemes, were inductively identified through the coding process. Discussion. This study found that the barriers residents and faculty face during EPA acquisition are similar to the challenges previously described in the pre-CBD assessment literature, suggesting that simply changing the assessment paradigm and modality will not necessarily alleviate pre-existing challenges to the assessment process or automatically improve assessment quality. Based on the results, a list of recommendations was generated as a starting point for change and improvement at a resident and faculty level, residency program leadership level, and institutional level.
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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 | 0.044 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".