The regional medical campus model and rural family medicine practice in British Columbia: a retrospective longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Regional medical campuses have been implemented across North America to address gaps in the physician workforce. We report findings from a study that examined the association between a combined model of regional medical campuses and students' decision to enter rural family medicine practice. METHODS: In 2004, the University of British Columbia added 2 regional medical campuses, 1 in a large population centre in a rural and coastal context and 1 in a medium-sized population centre in an isolated northern and rural context. Data were extracted from the University of British Columbia's Medical Education Database. Multivariable logistic regression examined the relationship of age, sex, rural background and campus location to students' choice of rural family medicine practice. RESULTS: There was an association between campus location and choice of family medicine versus other specialties. A rural background (odds ratio [OR] 2.59, 95% confidence interval [CI] 1.08-6.21) and training at either of the 2 regional medical campuses (OR 3.24, 95% CI 1.19-8.83 and OR 5.38, 95% CI 2.24-12.91) predicted rural family practice. INTERPRETATION: Choosing to practise family medicine in a rural location was associated with having a rural background and having trained at a regional medical campus. These early results suggest that a combined regional campus model in medical education contributes to the rural family practice workforce.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".