Expanding healthcare capacity in Canada: the potential of internationally trained physicians
Bibliographic record
Abstract
Canada's healthcare system is facing a severe shortage of doctors, leaving millions of Canadians struggling to access essential primary and specialist care. Despite substantial investment in healthcare, Canada still falls behind other OECD countries in having enough physicians to meet patient needs. This crisis, fueled by inadequate workforce planning, an aging population, and increasing physician burnout, has forced more patients to rely on emergency departments for basic care, driving up costs and reducing quality of service. Internationally trained physicians (ITPs) represent a significant yet underutilized resource. However, they encounter numerous barriers, including restrictive licensing practices, insufficient residency spots, and accreditation systems that occasionally value training length more than clinical performance or demonstrated competency. To address these urgent challenges, Canada should expand on competency-based accreditation methods, build on existing Practice Ready Assessment programs, create more residency placements for ITPs, and reduce bureaucratic hurdles. Taking immediate steps toward these reforms will improve healthcare access, patient outcomes, and ensure long-term sustainability of healthcare across 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".