Medical school service regions in Canada: exploring graduate retention rates across the medical education training continuum and into professional practice
Bibliographic record
Abstract
PURPOSE: To create medical school service regions and examine national in-region graduate retention patterns across the medical education continuum and into professional practice as one approach to advancing social accountability in medical education. METHODS: = 19,971) were obtained from a centralized data repository and used to analyze in-region retention rates by medical specialty across the training continuum and five years into professional practice. RESULTS: Spatial inequities were observed across medical school service regions. Graduate retention patterns also varied across service region groups and medical specialties. Quebec (86.5%) and Ontario (80.4%) had above-average retention rates across the medical education continuum. Family medicine had the highest retention rates from undergraduate to postgraduate training (81.9%), while psychiatry had the highest retention rate across the training continuum and into professional practice (71.2%). The Alberta and British Columbia service region group demonstrated high retention rates across the training continuum and into professional practice and medical specialties, except for retention from undergraduate to postgraduate medical education. CONCLUSION: This study highlights the importance of considering both medical specialty and practice location of graduates when planning and retaining the physician workforce. The observed retention patterns among graduates are a critical aspect of addressing societal needs and represent an intermediate step towards achieving health equity. Furthermore, graduate retention patterns serve as an outcome measure for schools to demonstrate their commitment to social accountability. Tracking and monitoring graduate outcomes may lead schools to actively collaborate with government agencies responsible for healthcare policy, which may ultimately improve physician workforce planning and promote more equitable healthcare access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".