The cost of health professionals' brain drain in Kenya
Bibliographic record
Abstract
BACKGROUND: Past attempts to estimate the cost of migration were limited to education costs only and did not include the lost returns from investment. The objectives of this study were: (i) to estimate the financial cost of emigration of Kenyan doctors to the United Kingdom (UK) and the United States of America (USA); (ii) to estimate the financial cost of emigration of nurses to seven OECD countries (Canada, Denmark, Finland, Ireland, Portugal, UK, USA); and (iii) to describe other losses from brain drain. METHODS: The costs of primary, secondary, medical and nursing schools were estimated in 2005. The cost information used in this study was obtained from one non-profit primary and secondary school and one public university in Kenya. The cost estimates represent unsubsidized cost. The loss incurred by Kenya through emigration was obtained by compounding the cost of educating a medical doctor and a nurse over the period between the average age of emigration (30 years) and the age of retirement (62 years) in recipient countries. RESULTS: The total cost of educating a single medical doctor from primary school to university is 65,997 US dollars; and for every doctor who emigrates, a country loses about 517,931 US dollars worth of returns from investment. The total cost of educating one nurse from primary school to college of health sciences is 43,180 US dollars; and for every nurse that emigrates, a country loses about 338,868 US dollars worth of returns from investment. CONCLUSION: Developed countries continue to deprive Kenya of millions of dollars worth of investments embodied in her human resources for health. If the current trend of poaching of scarce human resources for health (and other professionals) from Kenya is not curtailed, the chances of achieving the Millennium Development Goals would remain bleak. Such continued plunder of investments embodied in human resources contributes to further underdevelopment of Kenya and to keeping a majority of her people in the vicious circle of ill-health and poverty. Therefore, both developed and developing countries need to urgently develop and implement strategies for addressing the health human resource crisis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".