Retention in a 10-year cohort of internationally trained family physicians licensed in Manitoba.
Bibliographic record
Abstract
INTRODUCTION: International medical graduates (IMGs) seeking licensure in Canada have been recruited to practise in medically underserviced areas, but retention of these physicians remains a concern. This study explored retention of IMG family physicians in Manitoba and its predictors. METHODS: We used data from the University of Manitoba, provincial registries and Manitoba Health. Inclusion criteria were IMGs who completed University of Manitoba IMG training or assessment programs, and their return-of-service. Practice location, certification and licensure status were examined. We used logistic regression to consider the effects of a mentorship program, Manitoba residency at application, IMG program and years since program graduation on retention. RESULTS: = 0.007), explaining 10% of the variance in retention. Two predictors were significant: years since program graduation and Manitoba residency at the time of application. CONCLUSION: Long-term retention of IMG physicians remains a concern. Potential interventions likely to increase retention, such as Manitoba residency at application and a focus on mentorship programs, should be further explored.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".