Differences between international medical graduates and Canadian medical graduates in a medical learning environment: From matching to residency and beyond
Bibliographic record
Abstract
Objective: To determine the differences between international medical graduates (IMG) and canadian medical graduates (CMG) in the medical learning environment (MLE) as there is progression from matching to residency and beyond. Method: A narrative literature review was done using the search engines pubmed, medline and embase on publications from 2000 to 2021 comparing IMG to CMG and those that compared IMG to non IMG in international publications were also considered. Results: The IMGs are offered less residency program positions compared to CMGs during the CaRMS selection process and specifically less in specialty programs. Amongst the article, 66% of IMGs compared to 90% of CMG were successful in the certification examination of the college of family physicians of Canada. A US article on the other hand found similarities in performance of USMGs and IMGs in a surgical residency program. Conclusion: A lot of IMG face several challenges including perceived systemic and individual discrimination, lack of mentorship and poor ability to navigate after immigration even after they are matched into a Canadian residency program. These are significant issues that should be dealt with to enable increase success and survival of IMGs in the MLE.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".