Canadian and immigrant international medical graduates. Can Fam Physician
Bibliographic record
Abstract
OBJECTIVE To compare the demographic and educational characteristics of Canadian international medical graduates (IMGs) and immigrant IMGs who applied to the second iteration of the Canadian Resident Matching Service (CaRMS) match in 2002. DESIGN Web-based questionnaire survey. SETTING The study was conducted during the second-iteration CaRMS match in Canada. PARTICIPANTS The sampling frame included the entire population of IMG registrants for the 2002 CaRMS match in Canada who expressed interest in applying for a ministry-funded residency position in the 13 English-speaking Canadian medical schools. Those who immigrated to Canada with medical degrees were categorized as immigrant IMGs. Canadian citizens and landed immigrants or permanent residents who left Canada to obtain a medical degree in another country were defi ned as Canadian IMGs. MAIN OUTCOME MEASURES Demographic characteristics, education and training outside Canada, examinations taken, previous applications for a residency position, preferred type of practice, and barriers and supports were compared. RESULTS Out of 446 respondents who indicated their immigration status and education, 396 (88.8%) were immigrant IMGs and 50 (11.2%) were Canadian IMGs. Immigrant IMGs tended to be older, be married, and have dependent
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".