Not wanted on the voyage: highlighting intrinsic CanMEDS gaps in Competence by Design curricula
Bibliographic record
Abstract
BACKGROUND: As governing bodies design new curricula that seek to further incorporate principles of competency-based medical education within time-based models of training, questions have been raised regarding the continued centrality of existing CanMEDS competencies. Although efforts have been made to align these new curricula with CanMEDS, we don't yet know to what extent these competencies are meaningfully integrated. METHODS: A content analysis approach was used to systematically evaluate national Canadian curricula for 18 residency-training programs and determine the number of times each enabling CanMEDS competency was represented. RESULTS: Clear trends persisted across all programs. Medical Expert and Collaborator competencies were well integrated into curriculum (81% and 86% mapped to assessment) while competencies related to the Leader, Professional, and Health Advocate roles were less frequently mapped to assessment (41%, 36%, and 40%) and were often absent from the new curricula altogether (59%, 64%, and 60%). CONCLUSION: Deliberate planning in curriculum development affords the early identification of gaps. These gaps can inform current assessment practice and future curricular development by providing direction for innovation. If we are to ensure that any new curricula meaningfully address all CanMEDS roles, we need to think carefully about how to best teach and assess underrepresented competencies.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".