The Canadian International Medical Graduate Bottleneck: A New Problem for New Doctors
Bibliographic record
Abstract
Background: A growing population of Canadian students are travelling outside of Canada for medical training. The purpose of this study is to assess the opportunity for Canadians studying medicine abroad (CSAs) to secure post-graduate medical residency positions as International medical graduates (IMGs) in Canada.Methods: Current statistics on IMG applicants into the Canadian Residency Matching Service (CaRMS) will be compared to the number of CSAs applying to return to Canada.Results: In 2010, 75% (1232) of IMG applicants were unmatched following application to CaRMS, despite a doubling in positions reserved for IMGs from 2003. An estimated 3750 CSAs are currently attending over 55 medical schools globally; a six-fold increase since first reports in 2006. Between 2012 and 2014, it is estimated that 72.8% of CSAs will graduate, with 90.4% hoping to return to Canada for post-graduate residency training. Discussion: The increasing population of CSAs poses a significant risk for future IMGs attempting to secure postgraduate training positions in Canada. From this perspective, we have coined the term ‘Canadian IMG Bottleneck’ – which describes the funnelling effect that has been created by the growing number of CSAs and the limited number of IMG residency positions available in Canada.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".