Implementing competency‐based education in multiple programs: A workshop to structure and monitor programs' priorities using ADDIE
Bibliographic record
Abstract
Abstract Context All Canadian postgraduate medical programs are implementing the major components of competency‐based education. To do so, our institution had to provide individualized training and monitoring for all our programs. Innovation We organized half‐day workshops with four work sessions: core competencies, competency portfolio, curriculum mapping, and competence committee. Program teams decided their priority tasks after each work session. We classified tasks into ADDIE pedagogical design stages (Analysis, Design, Development, Implementation, Evaluation). We conducted interviews at 12 months. Results Programs (n = 29) prioritized mainly tasks of Design (37% of tasks), Development (24%) and Analysis (21%). At 12‐month follow‐up (n = 17), 20% of the tasks were initiated, 22% reached a higher stage, 33% reaching Implementation/Evaluation. Programs needed material and financial resources for Analysis/Development tasks, and faculty training for Implementation/Evaluation tasks. Conclusion Work sessions provided a structure to commit to priority tasks. Their classification into ADDIE stages systematized the monitoring and the search for solutions.
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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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".