Implementing the CanMEDS™ physician roles in rural specialist education: The multi-specialty community training network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it