The Canadian contribution to the US physician workforce
Bibliographic record
Abstract
BACKGROUND: A physician shortage has been declared in both Canada and the United States. We sought to examine the migration pattern of Canadian-trained physicians to the United States, the contribution of this migration to the Canadian physician shortage and policy options in light of competing shortages in both countries. METHODS: We performed a cross-sectional analysis of the 2004 and 2006 American Medical Association Physician Masterfiles, the 2002 Area Resource File and data from the Canadian Institute for Health Information, the Canadian Medical Association and the Association of Faculties of Medicine of Canada. We describe the migration pattern of Canadian medical school graduates to the United States, the number of Canadian-trained physicians in the United States in 2006, the proportion who were in active practice, the proportion who were practising in rural or underserved areas and the annual contribution of Canadian-trained physicians to the US physician workforce. RESULTS: Two-thirds of the 12 040 Canadian-educated physicians living in the United States in 2006 were practising in direct patient care, 1023 in rural areas. About 186, or 1 in 9, Canadian-educated physicians from each graduating class joined the US physician workforce providing direct patient care. Canadian-educated physicians are more likely than US-educated physicians to practise in rural areas. INTERPRETATION: Minimizing emigration, and perhaps recruiting physicians to return to Canada, could reduce physician shortages, particularly in subspecialties and rural areas. In light of competing physician shortages, it will be important to consider policy options that reduce emigration, improve access to care and reduce reliance on physicians from developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".