Down the brain drain: a rapid review exploring physician emigration from West Africa
Bibliographic record
Abstract
BACKGROUND: The emigration of physicians from low- and middle-income countries (LMICs) to high-income countries (HICs), colloquially referred to as the "brain drain", has been a topic of discussion in global health spheres for years. With the call to decolonize global health in mind, and considering that West Africa, as a region, is a main source of physicians emigrating to HICs, this rapid review aims to synthesize the reasons for, and implications of, the brain drain, as well as recommendations to mitigate physician emigration from West African countries to HICs. METHODS: A literature search was conducted on PubMed, EMBASE and The Cochrane Library. Main inclusion criteria were the inclusion of West African trained physicians' perspectives, the reasons and implications of physician emigration, and recommendations for management. Data on the study design, reasons for the brain drain, implications of brain drain, and proposed solutions to manage physician emigration were extracted using a structured template. The Hawker Tool was used as a risk of bias assessment tool to evaluate the included articles. RESULTS: A total of 17 articles were included in the final review. Reasons for physician emigration include poor working conditions and remuneration, limited career opportunities, low standards of living, and sociopolitical unrest. Implications of physician emigration include exacerbation of low physician to population ratios, and weakened healthcare systems. Recommendations include development of international policies that limit HICs' recruitment from LMICs, avenues for HICs to compensate LMICs, collaborations investing in mutual medical education, and incorporation of virtual or short-term consultation services for physicians working in HICs to provide care for patients in LMICs. CONCLUSIONS: The medical brain drain is a global health equity issue requiring the collaboration of LMICs and HICs in implementing possible solutions. Future studies should examine policies and innovative methods to involve both HICs and LMICs to manage the brain drain.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".