Evaluating Competence by Design as a Large System Change Initiative: Readiness, Fidelity, and Outcomes
Bibliographic record
Abstract
Program evaluation is an essential, but often neglected, activity in any transformational educational change. Competence by Design was a large-scale change initiative to implement a competency-based time-variable educational system in Canadian postgraduate medical education. A program evaluation strategy was an integral part of the build and implementation plan for CBD from the beginning, providing insights into implementation progress, challenges, unexpected outcomes, and impact. The Competence by Design program evaluation strategy was built upon a logic model and three pillars of evaluation: readiness to implement, fidelity and integrity of implementation, and outcomes of implementation. The program evaluation strategy harvested from both internally driven studies and those performed by partners and invested others. A dashboard for the program evaluation strategy was created to transparently display a real-time view of Competence by Design implementation and facilitate continuous adaptation and improvement. The findings of the program evaluation for Competence by Design drove changes to all aspects of the Competence by Design implementation, aided engagement of partners, supported change management, and deepened our understanding of the journey required for transformational educational change in a complex national postgraduate medical education system. The program evaluation strategy for Competence by Design provides a framework for program evaluation for any large-scale change in health professions education.
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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 | 0.299 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".