The financial cost of doctors emigrating from sub-Saharan Africa: human capital analysis
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".