Is there any solution to the "brain drain" of health professionals and knowledge from Africa?
Bibliographic record
Abstract
African public health care systems suffer from significant "brain drain" of its health care professionals and knowledge as health workers migrate to wealthier countries such as Australia, Canada, USA, and the United Kingdom. Knowledge generated on the continent is not readily accessible to potential users on the continent. In this paper, the brain drain is defined as both a loss of health workers (hard brain drain) and unavailability of research results to users in Africa (soft brain drain). The "pull" factors of "hard brain drain" include better remuneration and working conditions, possible job satisfaction, and prospects for further education, whereas the "push" factors include a lack of better working conditions including promotion opportunities and career advancement. There is also a lack of essential equipment and non-availability or limited availability of specialist training programs on the continent. The causes of "soft brain drain" include lack of visibility of research results in African journals, better prospects for promotion in academic medicine when a publication has occurred in a northern high impact journal, and probably a cultural limitation because many things of foreign origin are considered superior. Advocates are increasingly discussing not just the pull factors but also the "grab" factors emanating from the developed nations. In order to control or manage the outflow of vital human resources from the developing nations to the developed ones, various possible solutions have been discussed. The moral regard to this issue cannot be under-recognized. However, the dilemma is how to balance personal autonomy, right to economic prosperity, right to personal professional development, and the expectations of the public in relation to adequate public health care services in the developing nations.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".