What Do We Know and Not Know about the Professional Integration of International Medical Graduates (IMGs) in Canada?
Bibliographic record
Abstract
BACKGROUND: The literature on international medical graduates (IMGs) in Canada is growing, but there is a lack of systematic analysis of the literature. OBJECTIVES: To examine (1) the major themes in academic and grey literature pertaining to professional integration of IMGs in Canada; and (2) the gaps in our knowledge on integration of IMGs. METHODS: This paper is based on the scoping review of academic and grey literature published during 2001-2013 about IMGs in Canada. RESULTS: The literature on IMGs focuses on (1) pre-immigration activities; (2) early-arrival activities; (3) credential recognition/professional recertification; (4) bridging and residency training; (5) workplace integration; and (6) alternative paths to integration. The gaps in the literature include pre-immigration and early-arrival activities, and alternative paths for integration for those IMGs who do not pursue medical license. CONCLUSION: Pre-immigration and early-arrival activities and alternative career paths for IMGs should be addressed in academic and policy research.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".