Developing Partnerships for Distributed Community-Engaged Medical Education in Northern Ontario, Canada
Bibliographic record
Abstract
The Northern Ontario School of Medicine (NOSM) was established as a not-for-profit medical education corporation in November 2002 with a social accountability mandate to provide "undergraduate and post graduate medical education programs that are innovative and responsive to the individual needs of students and to the healthcare needs of the people in Northern Ontario."[1] NOSM is not only the first new medical school in Canada in 30 years; it is also the first medical school established in, for and about the Northern Ontario region; and the first Canadian dual university medical school. In practice, these "firsts" constitute community-engaged medical education programs distributed in 70 communities across Northern Ontario, made possible by partnerships with universities, advisory groups, community organizations, hospitals and clinics. It is through these partnerships that NOSM works to fully achieve its social accountability mandate with a diverse, multilingual population, dispersed over a wide geographic area. Northern Ontario is a mostly rural, densely forested area of 820,000 square kilometers (approximately the size of France and Germany combined) with a population of just over 800,000, including First Nations (Aboriginal), Francophone and Anglophone groups. In general, the largest First Nation populations are located on reserves in the Northwest, while Francophone populations are generally concentrated in the Northeast. In April 2007, the Ministry of Health and Long-Term Care designated 37 Northern Ontario communities (including some larger communities such as North Bay and Thunder Bay) as medically "underserviced" with a total shortage of 132 family physicians.[2] In addition, 14 Northern Ontario communities were designated as underserviced in specialists with a total shortage of 129.[3].
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".