Program Evaluation in Competence by Design: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: The evaluation of Competence by Design (CBD) residency programs is crucial for enhancing program effectiveness. However, literature on evaluating CBD programs is limited. We conducted a 2-phase mixed-methods study to (a) assess the extent of program evaluation activities in CBD residency programs in Canada, (b) explore reasons for engaging or not engaging in these activities, (c) examine how CBD programs are conducting program evaluations, and (d) identify ways to build capacity for program evaluation. METHODS: Phase 1 involved surveying 149 program directors from specialty/subspecialty programs that transitioned to CBD between 2017 and 2020. We calculated descriptive statistics for 22 closed-ended survey items. Phase 2 comprised interviews with a subset of program directors from Phase 1. Data analysis followed a 3-step iterative process: data condensation, data display, and drawing and verifying conclusions. RESULTS: In Phase 1, we received 149 responses, with a 33.5% response rate. Of these, 127 (85.2%) indicated their programs engage in evaluation, while 22 (14.8%) do not. Among the 127 programs that engage in evaluation, 29 (22.8%) frequently or always develop evaluation questions, and 23 (18.1%) design evaluation proposals/plans. Reasons for engaging in evaluation included decision-making and stimulating changes in educational practices. Conversely, reasons for not engaging included lack of knowledge, personnel, and funding. In Phase 2, 15 program directors were interviewed. They reported that CBD programs face challenges such as limited resources and buy-in, rely on ad hoc evaluation methods, and use a team-based evaluation format. To enhance evaluation capacities, interviewees suggested (a) developing expertise in program evaluation, (b) acquiring evaluation resources, and (c) advocating for clear evaluation expectations. CONCLUSIONS: Most CBD residency programs are engaged in program evaluations, but the quality is often questionable. To fully realize the potential of program evaluation, CBD programs need additional resources and support to improve evaluation practices and outcomes.
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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 | 0.163 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".