African nurses on the move: decisions, destinations and recruitment practices - a scoping review
Bibliographic record
Abstract
BACKGROUND: The transnational migration of African nurses negatively impacts nurse-to-population ratios and life expectancy indices in many African countries. Understanding migration decisions, destination preferences, and recruitment practices of African nurses is crucial for identifying appropriate and effective retention interventions. OBJECTIVE: The objectives of this scoping review are to examine the state of evidence in relation to the decisions surrounding international African nurse migration, as well as destinations preferences and recruitment practices employed to attract African nurses. METHODS: Guided by the updated Joanna Briggs Institute (JBI) methodology for scoping reviews, we conducted a comprehensive search on empirical studies and grey literature on African nurse migration published in English from 2000 onwards and indexed in health and interdisciplinary databases. Studies on African nurse or student nurse migration intention were excluded. RESULTS: We included 28 studies, twenty-one of which were peer-reviewed and seven from the grey literature. Synthesis of included studies found that international African nurse migration is influenced by economic challenges and income disparities, and career dynamics and job sustainability in home countries. The choice of destination by African nurses is impacted by African countries' past colonial relationships with destination countries, linguistic and cultural similarities. African nurses are recruited through international inter-agency collaboration and via direct recruitment by destination country health systems. CONCLUSION: Low income, poor economic growth and inadequate investment in African health systems significantly drive African nurse emigration, complicating efforts to attain universal health coverage. Recruitment strategies for nurse from African are often unregulated and can lead to exploitation and human trafficking. Again, as African nurse migration continues to rise, further studies are needed to examine their migration and transition experiences, as well as the support systems available in their destinations. Finally, improving workforce policies to meet the evolving needs of nurses is vital for retaining nurses in Africa.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".