Development, Implementation, and Meta-Evaluation of a National Approach to Programmatic Assessment in Canadian Family Medicine Residency Training
Bibliographic record
Abstract
The growing international adoption of competency-based medical education has created a desire for descriptions of innovative assessment approaches that generate appropriate and sufficient information to allow for informed, defensible decisions about learner progress. In this article, the authors provide an overview of the development and implementation of the approach to programmatic assessment in postgraduate family medicine training programs in Canada, called Continuous Reflective Assessment for Training (CRAFT). CRAFT is a principles-guided, high-level approach to workplace-based assessment that was intentionally designed to be adaptable to local contexts, including size of program, resources available, and structural enablers and barriers. CRAFT has been implemented in all 17 Canadian family medicine residency programs, with each program taking advantage of the high-level nature of the CRAFT guidelines to create bespoke assessment processes and tools appropriate for their local contexts. Similarities and differences in CRAFT implementation between 5 different family medicine residency training programs, representing both English- and French-language programs from both Western and Eastern Canada, are described. Despite the intentional flexibility of the CRAFT guidelines, notable similarities in assessment processes and procedures across the 5 programs were seen. A meta-evaluation of findings from programs that have published evaluation information supports the value of CRAFT as an effective approach to programmatic assessment. While CRAFT is currently in place in family medicine residency programs in Canada, given its adaptability to different contexts as well as promising evaluation data, the CRAFT approach shows promise for application in other training environments.
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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 | 0.403 | 0.513 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".