Impact of the rural pipeline in medical education: practice locations of recently graduated family physicians in Ontario
Bibliographic record
Abstract
BACKGROUND: The "rural pipeline" suggests that students educated in rural, or other underserviced areas, are more likely to establish practices in such locations. It is upon this concept that the Northern Ontario School of Medicine (NOSM) was founded. Our analysis answers the following question: Are physicians who were educated at NOSM more likely to practice in rural and northern Ontario compared with physicians who were educated at other Canadian medical schools? METHODS: We used data from the College of Physicians and Surgeons of Ontario. We compared practice locations of certified Ontario family physicians who had graduated from NOSM vs. other Canadian medical schools in 2009 or later. We categorized the physicians according to where they completed their undergraduate (UG) and postgraduate (PG) training, either at NOSM or elsewhere. We used logistic regression models to determine if the location of UG and PG training was associated with rural or northern Ontario practice location. RESULTS: Of the 535 physicians examined, 67 had completed UG and/or PG medical education at NOSM. Over two thirds of physicians with any NOSM education were practicing in northern areas and 25.4% were practicing in rural areas of Ontario compared with those having no NOSM education, with 4.3 and 10.3% in northern and rural areas, respectively. Physicians who graduated from NOSM-UG were more likely to have practices located in rural Ontario (OR = 2.57; p = 0.014) whereas NOSM-PG physicians were more likely to have practices in northern Ontario (OR = 57.88; p < 0.001). CONCLUSIONS: NOSM education was associated with an increased likelihood of practicing in rural (NOSM-UG) and northern (NOSM-PG) 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".