Updating CanMEDS in 2015: Ensuring Quality in the Change Process
Bibliographic record
Abstract
Background: The Royal College of Physicians and Surgeons of Canada (RCPSC) will launch CanMEDS 2015 after a 3-year process to revise and refine the CanMEDS 2005 Framework. To engage multiple stakeholders, the RCPSC distributed an online survey to its fellows, trainees, and other interested stakeholders in 2013.Methods: Educators at the RCPSC developed a survey using an iterative, reflective process. The mixed methods survey, administered electronically, consisted of open- and close-ended questions related to the 7 CanMEDS Roles. The results were analyzed by 2 RCPSC clinician educators and 2 RCPSC education scientists.Results: Of 1204 respondents, 60% were fellows and 19% were trainees, representing all but 2 disciplines within the RCPSC. Forty-seven percent were university-based and 64% considered themselves to either know the framework or be an expert in applying 1 role. The depth and breadth of responses varied greatly and qualitative analysis revealed 5 overarching themes: simplify the descriptions of roles; clarify the roles; reduce overlap between roles; assist with implementation; and include patient safety.Conclusions: Stakeholders are very interested in participating in the revision and renewal of the CanMEDS Framework. Responses align with themes found through other consultation methods and validate the importance of reaching out to stakeholders in the process of framework revision.
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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 | 0.261 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".