Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore sub-Saharan African nurses' reasons for moving to the UK, their views on the skills and brain drain, and what can be done to stem the situation. BACKGROUND: The UK and other developed nations such as the USA, Canada and Australia have been recruiting internationally qualified nurses including those from sub-Saharan African, which has raised concerns of skills and brain drain from these countries that are known to suffer from nurse shortages. METHODS: A purposeful sample of 30 nurses from sub-Saharan African was drawn from four National Health Service trusts in the north-east of England. Using focus group discussions and personal interviews, the study explored and examined nurses' views on their motivation to move to the developed countries and what can be done to reduce nurse migration from sub-Saharan African and give those countries a chance to develop their health systems by retaining their health personnel. RESULTS: Five main themes emerged from data analysis: poor remuneration, lack of professional development in the home countries, poor health care and systems, language and education similarities and easy availability of jobs and visas. CONCLUSION: Data indicate that migration motives for nurses are complex and inherent in historical links and in global values. Nurses stressed that they would like to stay in their own countries and help develop healthcare there, but reasons for moving were often strong and apparently not within their control. RELEVANCE TO CLINICAL PRACTICE: Nurse migration from sub-Saharan African has often been cited as a limitation in providing effective healthcare in those countries. Delineating motivational factors for nurses could help to stem this migration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".