Transforming health professional education through social accountability: Canada's Northern Ontario School of Medicine
Bibliographic record
Abstract
BACKGROUND: The Northern Ontario School of Medicine (NOSM) has a social accountability mandate to contribute to improving the health of the people and communities of Northern Ontario. NOSM recruits students from Northern Ontario or similar backgrounds and provides Distributed Community Engaged Learning in over 70 clinical and community settings located in the region, a vast underserved rural part of Canada. METHODS: NOSM and the Centre for Rural and Northern Health Research (CRaNHR) used mixed methods studies to track NOSM medical learners and dietetic interns, and to assess the socioeconomic impact of NOSM. RESULTS: Ninety-one percent of all MD students come from Northern Ontario with substantial inclusion of Aboriginal (7%) and Francophone (22%) students. Sixty-one percent of MD graduates have chosen family practice (predominantly rural) training. The socioeconomic impact of NOSM included new economic activity, more than double the School's budget; enhanced retention and recruitment for the universities and hospital/health services; and a sense of empowerment among community participants attributable in large part to NOSM. DISCUSSION: There are signs that NOSM is successful in graduating health professionals who have the skills and desire to practice in rural/remote communities and that NOSM is having a largely positive socioeconomic impact on Northern Ontario.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".