Implementing the CanMEDS™ physician roles in rural specialist education: The multi-specialty community training network
Bibliographic record
Abstract
CONTEXT: Changing medical education to realign it with societal needs has become a renewed priority in many countries. Advanced training in rural settings to prepare physicians to better serve rural areas has received particular attention around the world. Such initiatives are usually targeted at primary care practitioners. Few initiatives have been designed to enhance specialist training in a rural setting, let alone adapt specialist competency frameworks such as the CanMEDS roles of the Royal College of Physicians and Surgeons of Canada to non-urban medical education. ISSUE: We describe an innovation in medical training for rural competence for specialist physicians using the CanMEDS framework near London, Ontario, Canada. Since 1997, the University of Western Ontario has established its Multi-Specialty Community Training Network (MSCTN) to provide rural and regional training opportunities for specialty residents in anaesthesia, general surgery, internal medicine, paediatrics, obstetrics and psychiatry. It became the first program in Canada to fully adapt the new CanMEDS roles into learning objectives and evaluations. LESSONS LEARNED: Competency-based frameworks like CanMEDS are important because they provide a comprehensive tool to organize outcome-based curricula. The CanMEDS roles framework has been very useful in developing educational goals for rural/regional specialty resident rotations as well as forming a constructive basis for resident, preceptor, and program evaluations. Our experiences with this program may provide lessons for others planning training for specialists in rural settings, and those adopting the CanMEDS competency framework.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".