Retention of visa-trainee post-graduate residents in Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Visa trainees (international medical graduates [IMG] who train in Canada under a student or employment visa) are expected to return home after completing their training. We examine the retention patterns of visa trainee residents funded by Canadian (regular ministry and other), foreign, or mixed sources. METHODS: We linked data from the Canadian Post-MD Medical Education Registry with Scott's Medical Database for a retrospective cohort study. Eligible trainees were IMG visa trainees as of their first year of training, started their residency program no earlier than 2000, and exited training between 2006 and 2016. We used Cox regression to compare the retention of visa trainees by funding source. RESULTS: Of 1,913 visa trainees, 431(22.5%), 1353 (70.7%) and 129 (6.8%) had Canadian, foreign, or mixed funding, respectively. The proportion of trainees remaining in Canada decreased over time, with 35.5% (679/1913); 17.7% (186/1052); 10.8% (11/102) in Canada one, five, and ten years, respectively after their exit from PGME training. Trainees who remained on visas (HR: 1.91; [95% CI 1.59, 2.30]), were funded exclusively by foreign sources (HR: 1.46; [95% CI 1.25, 1.69]), and who had graduated from 'Western' countries (HR: 1.39; [95% CI 1.06, 1.84]) were more likely to leave Canada compared to trainees who became citizens/permanent residents, were funded by Canadian sources, or were visa graduates of Canadian medical schools, respectively. CONCLUSIONS: Most visa trainees leave Canada following their training. Trainees with Canadian connections (funding and/or change in legal status) were more likely to remain in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".