Too Poor to Leave, Too Rich to Stay: Developmental and Global Health Correlates of Physician Migration to the United States, Canada, Australia, and the United Kingdom
Bibliographic record
Abstract
OBJECTIVES: We analyzed the relationship between physician migration from developing source countries to more developed host countries (brain drain) and the developmental and global health profiles of source countries. METHODS: We used a cross-section of 141 countries that lost emigrating physicians to the 4 major destinations: the United States, Canada, Australia, and the United Kingdom. For each source country, we defined physician migration density as the number of migrant physicians per 1000 population practicing in any of the 4 major destination countries. RESULTS: Source countries with better human resources for health, more economic and developmental progress, and better health status appear to lose proportionately more physicians than the more disadvantaged countries. Higher physician migration density is associated with higher current physician (r=0.42, P< .001), nurse (r=0.27, P=.001), and public health (r=0.48, P=.001) workforce densities and more medical schools (r=0.53, P<.001). CONCLUSIONS: Policymakers should realize that physician migration is positively related to better health systems and development in source countries. In view of the "train, retain, and sustain" perspective of public health workforce policies, physician retention should become even more important to countries growing richer, whereas poorer countries must invest more in training policies.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".