Fulfilling a social mission: examining practice locations of residency graduates over two decades
Bibliographic record
Abstract
Background: Medical schools play a critical role in shaping the physician workforce. Tracking the practice locations of medical graduates is essential for addressing healthcare disparities and workforce shortages in underserved regions. This study examines the geographic distribution of residency graduates from a Canadian francophone university, aligning their practice locations with the university's social accountability mandate. Methods: A cross-sectional descriptive study was conducted using data from the Canadian Post-M.D. Education Registry (CAPER) for 2,410 residency graduates (2000-2020) from 35 residency training programs. We analyzed practice locations at two-, five-, and 10-years post-graduation across medical specialties, sex, and geographic region, with a focus on Quebec's administrative health regions. Results: There were 2,410 graduates from 35 residency training programs. Family medicine accounted for 57.8% of all graduates and 42.2% were from all other specialties. Most graduates (77.7%) practiced in the province of Quebec, with concentrations in the regions of the Eastern Townships (19.4%), Montérégie (14.6%), and Saguenay-Lac-St-Jean (7.6%). Conclusion: This study demonstrates the important regional impact of the university's role in training family physicians and addressing healthcare needs in Quebec. The findings suggest the importance of tracking to inform evidence-based workforce planning and policy development. Medical schools can leverage such data to align training programs with societal health needs and enhance their contributions to regional healthcare systems.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".