The financial cost of doctors emigrating from sub-Saharan Africa: human capital analysis
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Bibliographic record
Abstract
OBJECTIVE: To estimate the lost investment of domestically educated doctors migrating from sub-Saharan African countries to Australia, Canada, the United Kingdom, and the United States. DESIGN: Human capital cost analysis using publicly accessible data. SETTINGS: Sub-Saharan African countries. PARTICIPANTS: Nine sub-Saharan African countries with an HIV prevalence of 5% or greater or with more than one million people with HIV/AIDS and with at least one medical school (Ethiopia, Kenya, Malawi, Nigeria, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe), and data available on the number of doctors practising in destination countries. MAIN OUTCOME MEASURES: The financial cost of educating a doctor (through primary, secondary, and medical school), assuming that migration occurred after graduation, using current country specific interest rates for savings converted to US dollars; cost according to the number of source country doctors currently working in the destination countries; and savings to destination countries of receiving trained doctors. RESULTS: In the nine source countries the estimated government subsidised cost of a doctor's education ranged from $21,000 (£13,000; €15,000) in Uganda to $58,700 in South Africa. The overall estimated loss of returns from investment for all doctors currently working in the destination countries was $2.17bn (95% confidence interval 2.13bn to 2.21bn), with costs for each country ranging from $2.16m (1.55m to 2.78m) for Malawi to $1.41bn (1.38bn to 1.44bn) for South Africa. The ratio of the estimated compounded lost investment over gross domestic product showed that Zimbabwe and South Africa had the largest losses. The benefit to destination countries of recruiting trained doctors was largest for the United Kingdom ($2.7bn) and United States ($846m). CONCLUSIONS: Among sub-Saharan African countries most affected by HIV/AIDS, lost investment from the emigration of doctors is considerable. Destination countries should consider investing in measurable training for source countries and strengthening of their health systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it