Determining the weighting and relative importance of CanMEDS roles and competencies
Bibliographic record
Abstract
BACKGROUND: The CanMEDS roles and competencies are being used as the framework to support the development of the Manitoba Practice Assessment Program (MPAP) designed to assess the competence of physicians practicing with a conditional license. Establishing the link between clinical practice and assessment of performance is critical in the development of the MPAP. A first step in establishing this link is to identify activities performed in actual clinical practice as well as the importance of those activities. METHODS: A descriptive survey design was used to answer the research questions: (1) How do physicians rate the complexity, frequency, and criticality of CanMEDS roles? (2) What is the distribution of perceived importance scores for the CanMEDS roles? Two online surveys, one specific to family practice physicians, and one specific to specialists, were emailed to a sample of Canadian physicians. RESULTS: Overall perceived importance scores were calculated for each of the CanMEDS roles. It appears that each role is considered to be at least moderately important. The Medical Expert role was ranked as the most important, followed by the roles of Communicator, Professional, Collaborator, Scholar, Manager, and Health Advocate. There were no significant differences in overall CanMEDS perceived importance scores between family practice physicians and specialists (N = 88). CONCLUSIONS: Given that each of the CanMEDS roles is considered at least moderately important, a variety of assessment tools are needed to evaluate competencies across the entire spectrum of roles. The results underscore the importance of incorporating a multifaceted approach when developing a practice assessment program.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".