The migration of physicians from sub-Saharan Africa to the United States of America: measures of the African brain drain
Bibliographic record
Abstract
BACKGROUND: The objective of this paper is to describe the numbers, characteristics, and trends in the migration to the United States of physicians trained in sub-Saharan Africa. METHODS: We used the American Medical Association 2002 Masterfile to identify and describe physicians who received their medical training in sub-Saharan Africa and are currently practicing in the USA. RESULTS: More than 23% of America's 771 491 physicians received their medical training outside the USA, the majority (64%) in low-income or lower middle-income countries. A total of 5334 physicians from sub-Saharan Africa are in that group, a number that represents more than 6% of the physicians practicing in sub-Saharan Africa now. Nearly 86% of these Africans practicing in the USA originate from only three countries: Nigeria, South Africa and Ghana. Furthermore, 79% were trained at only 10 medical schools. CONCLUSIONS: Physician migration from poor countries to rich ones contributes to worldwide health workforce imbalances that may be detrimental to the health systems of source countries. The migration of over 5000 doctors from sub-Saharan Africa to the USA has had a significantly negative effect on the doctor-to-population ratio of Africa. The finding that the bulk of migration occurs from only a few countries and medical schools suggests policy interventions in only a few locations could be effective in stemming 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".