Bibliographic record
Abstract
BACKGROUND: There has been substantial immigration of physicians to developed countries, much of it coming from lower-income countries. Although the recipient nations and the immigrating physicians benefit from this migration, less developed countries lose important health capabilities as a result of the loss of physicians. METHODS: Data on the countries of origin, based on countries of medical education, of international medical graduates practicing in the United States, the United Kingdom, Canada, and Australia were obtained from sources in the respective countries and analyzed separately and in aggregate. With the use of World Health Organization data, I computed an emigration factor for the countries of origin of the immigrant physicians to provide a relative measure of the number of physicians lost by emigration. RESULTS: International medical graduates constitute between 23 and 28 percent of physicians in the United States, the United Kingdom, Canada, and Australia, and lower-income countries supply between 40 and 75 percent of these international medical graduates. India, the Philippines, and Pakistan are the leading sources of international medical graduates. The United Kingdom, Canada, and Australia draw a substantial number of physicians from South Africa, and the United States draws very heavily from the Philippines. Nine of the 20 countries with the highest emigration factors are in sub-Saharan Africa or the Caribbean. CONCLUSIONS: Reliance on international medical graduates in the United States, the United Kingdom, Canada, and Australia is reducing the supply of physicians in many lower-income countries.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".