Examination outcomes and work locations of international medical graduate family medicine residents in Canada.
Bibliographic record
Abstract
OBJECTIVE: To describe the postgraduate medical education (PGME) examination outcomes and work locations of international medical graduates (IMGs); and to identify differences between Canadians studying abroad (CSAs) and non-CSAs. DESIGN: Cohort study using data from the National IMG Database and Scott's Medical Database. SETTING: Canada. PARTICIPANTS: All IMGs who had first entered a family medicine residency program between 2005 and 2009, with the exclusion of US graduates, visa trainees, and fellowship trainees. MAIN OUTCOME MEASURES: We examined 4 outcomes: passing the Medical Council of Canada Qualifying Examination Part 2 (MCCQE2), obtaining Certification in Family Medicine (CCFP), working in Canada within 2 years of completing PGME training, and working in Canada in 2015. RESULTS: Of the 876 residents in the study, 96.1% passed the MCCQE2, 78.1% obtained a specialty designation, 37.7% worked in Canada within 2 years after their PGME, and 91.2% worked in Canada in 2015. Older graduates were more likely (odds ratio [OR] = 3.45; 95% CI 1.52 to 7.69) than recent graduates were to pass the MCCQE2, and residents who participated in a skills assessment program before their PGME training were more likely (OR = 9.60; 95% CI 1.29 to 71.63) than those who had not were to pass the MCCQE2. Women were more likely (OR = 1.67; 95% CI 1.20 to 2.33) to obtain a specialty designation than men were. Recent graduates were more likely (OR = 1.36; 95% CI 1.03 to 1.79) than older graduates were to work in Canada following training. Residents who were eligible for a full licence were more likely (OR = 3.72; 95% CI 2.30 to 5.99) to work in Canada in 2015 than those who were not eligible for a full licence were. CONCLUSION: While most IMGs who entered the family medicine PGME program passed the MCCQE2, 1 in 5 did not obtain Certification. Most IMG residents remain in Canada. Canadians studying abroad and non-CSA IMGs share similar examination success rates and retention rates.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".