Feedback and formative assessment in Competency by Design: The experience of residents and supervisors within a urology training program
Bibliographic record
Abstract
INTRODUCTION: Competency by Design (CBD) is a form of competency-based medical education implemented in Canadian urology programs since 2018. Regular, multimethod assessments and formative feedback via Entrustable Professional Activities (EPAs) are the cornerstones of CBD. Personalized and regular feedback are the top perceived benefits of CBD by both residents and supervisors; however, evidence shows that in practice, constant feedback-seeking is burdensome, and increased quantity of feedback does not equal increased quality. The experience of CBD implementation has not yet been studied in surgical programs. Our aim was to examine how supervisors and residents have experienced the integration of formative assessment and feedback since the implementation of CBD in a surgical training program. METHODS: Using data from focus groups, a qualitative phenomenological analysis based on the experiences of the residents and supervisors in a urology residency program was performed. RESULTS: Residents and supervisors felt that CBD allowed for better tracking of resident performance and increased quantity of feedback; however, increased workload, delayed completion of EPA assessments, lack of direct observation in non-surgical activities, variable supervisor guidance, and lack of understanding of CBD were cited as barriers to providing proper feedback and formative assessment. CONCLUSIONS: The participants experienced a lukewarm transition in feedback and formative assessment practices with CBD. As with every process of change, these growing pains may eventually result in meaningful practice improvements and incorporation of a CBD culture into everyday learning activities.
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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 | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".