Migration of International Medical Graduates: Implications for the Brain-Drain
Bibliographic record
Abstract
Studies indicate that about 23 percent to 28 percent of the physicians working and residing in the United States, Canada, Australia, the UK and New Zealand were born and trained in the low-income countries, areas suffering from critical shortages of physicians and other health workers. In the US alone, the preponderance of the foreign physicians hails from South Africa, Philippines, India, Pakistan, and Nigeria. From Africa alone where the burden of disease, poverty, deprivation and death are greatest, around 23,000 qualified physicians emigrate annually. From the perspectives of the low-income countries, significant amounts of resources are, by necessity, committed into turning their nationals into vital intellectual capital for their own desperately needed health needs and crumbling healthcare systems. Thus, the migration of these physicians to other nations to help strengthen their already stable health care systems is not only ethically deplorable but poses moral hazards for both the physicians and the high-income countries. That is, high-income countries such as the United States, Canada, UK, Australia and New Zealand are draining the scarce recourses of the low-income countries through the loss of intellectual capital, a phenomenon that socio-economic and developmental experts have dubbed “the brain drain”.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".