Exploring the Contributions of Combined Model Regional Medical Education Campuses to the Physician Workforce
Bibliographic record
Abstract
PURPOSE: Physician shortages and maldistribution, particularly within family medicine, have led many medical schools worldwide to create regional medical campuses (RMCs) for clerkship training. However, Canadian medical schools have developed a number of RMCs in which all years of training (i.e., a combined model that includes both preclerkship and clinical training) are provided geographically separate from the main campus. This study addresses the question: Are combined model RMC graduates more likely to enter postgraduate training in family medicine and rural-focused programs relative to main campus graduates? METHOD: The authors used a quasi-experimental research design and analyzed 2006-2016 data from the Canadian Resident Matching Service (CaRMS). Graduating students (N = 26,525) from 16 Canadian medical schools who applied for the CaRMS match in their year of medical school graduation were eligible for inclusion. The proportions of graduates who matched to postgraduate training in (1) family medicine and (2) rural-focused programs were compared for combined model RMCs and main campuses. RESULTS: Of RMC graduates, 48.4% matched to family medicine (95% confidence interval [CI] = 46.1-50.7) compared with 37.1% of main campus graduates (95% CI = 36.5-37.7; P < .001). Of RMC graduates, 23.9% matched to rural-focused training programs (95% CI = 21.8-25.9) compared with 10.4% of main campus graduates (95% CI = 10.0-10.8; P < .001). Subanalyses ruled out a variety of potentially confounding variables. CONCLUSIONS: Combined model RMCs, in which all years of training take place away from the medical school's main campus, are associated with greater proportions of medical students entering family medicine postgraduate training and rural-focused training programs. These findings should encourage policymakers, health services agencies, and medical schools to continue seeking complements to academic medical center-based medical education.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".