The financial losses from the migration of nurses from Malawi
Bibliographic record
Abstract
BACKGROUND: The migration of health professionals trained in Africa to developed nations has compromised health systems in the African region. The financial losses from the investment in training due to the migration from the developing nations are hardly known. METHODS: The cost of training a health professional was estimated by including fees for primary, secondary and tertiary education. Accepted derivation of formula as used in economic analysis was used to estimate the lost investment. RESULTS: The total cost of training an enrolled nurse-midwife from primary school through nurse-midwifery training in Malawi was estimated as US$ 9,329.53. For a degree nurse-midwife, the total cost was US$ 31,726.26. For each enrolled nurse-midwife that migrates out of Malawi, the country loses between US$ 71,081.76 and US$ 7.5 million at bank interest rates of 7% and 25% per annum for 30 years respectively. For a degree nurse-midwife, the lost investment ranges from US$ 241,508 to US$ 25.6 million at 7% and 25% interest rate per annum for 30 years respectively. CONCLUSION: Developing countries are losing significant amounts of money through lost investment of health care professionals who emigrate. There is need to quantify the amount of remittances that developing nations get in return from those who migrate.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".