Program Evaluation Activities in Competence by Design: A Survey of Specialty/Subspecialty Program Directors
Bibliographic record
Abstract
Abstract Background The evaluation of Competence by Design (CBD) residency programs is essential for improving program effectiveness. There is limited literature on the evaluation of CBD programs. We investigated the extent to which program evaluation activities are occurring in CBD residency programs in Canada and the reasons why these programs are engaging or not engaging in them. Methods We surveyed program directors whose programs transitioned to CBD. We calculated descriptive statistics for the 22 closed-ended survey items. Results We obtained 149 responses (response rate 33.5%). Of the 149 respondents, 127 (85.2%) indicated that their programs do engage in evaluation while 22 (14.8%) indicated that their programs do not. Of the 127 whose programs do engage in evaluation, 29 (22.8%) reported that their programs frequently or always develop evaluation questions and 23 (18.1%) noted that their programs design evaluation proposals/plans. Reasons for engaging in evaluation included: to make decisions about the program, and to stimulate changes in educational practices. Reasons for not engaging in evaluation comprised: no knowledge on how to do it, no personnel to do evaluation, and no funding to do it. Conclusions While most CBD programs are doing program evaluation the quality of it is questionable.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".