Credentialing and retention of visa trainees in post-graduate medical education programs in Canada
Bibliographic record
Abstract
BACKGROUND: Visa trainees are international medical graduates (IMG) who come to Canada to train in a post-graduate medical education (PGME) program under a student or employment visa and are expected to return to their country of origin after training. We examined the credentialing and retention of visa trainees who entered PGME programs between 2005 and 2011. METHODS: Using the Canadian Post-MD Education Registry's National IMG Database linked to Scott's Medical Database, we examined four outcomes: (1) passing the Medical Council of Canada Qualifying Examination Part 2 (MCCQE2), (2) obtaining a specialty designation (CCFP, FRCPC/SC), and (3) working in Canada after training and (4) in 2015. The National IMG Database is the most comprehensive source of information on IMG in Canada; data were provided by physician training and credentialing organizations. Scott's Medical Database provides data on physician locations in Canada. RESULTS: There were 233 visa trainees in the study; 39.5% passed the MCCQE2, 45.9% obtained a specialty designation, 24.0% worked in Canada after their training, and 53.6% worked in Canada in 2015. Family medicine trainees (OR = 8.33; 95% CI = 1.69-33.33) and residents (OR = 3.45; 95% CI = 1.96-6.25) were more likely than other specialist and fellow trainees, respectively, to pass the MCCQE2. Residents (OR = 7.69; 95% CI = 4.35-14.29) were more likely to obtain a specialty credential than fellows. Visa trainees eligible for a full license were more likely than those not eligible for a full license to work in Canada following training (OR = 3.41; 95% CI = 1.80-6.43) and in 2015 (OR = 3.34; 95% CI = 1.78-6.27). CONCLUSIONS: Visa training programs represent another route for IMG to qualify for and enter the physician workforce in Canada. The growth in the number of visa trainees and the high retention of these physicians warrant further consideration of the oversight and coordination of visa trainee programs in provincial and in pan-Canadian physician workforce planning.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".