International Recruitment of Nurses: Policy and Practice in the United Kingdom
Bibliographic record
Abstract
OBJECTIVE: To synthesize information about nurse migration into and out of the United Kingdom in the period to 2005, and to assess policy implications. PRINCIPAL FINDINGS: There has been rapid growth in inflow of nurses to the United Kingdom from other countries. In recent years, 40-50 percent of new nurse registrants in the United Kingdom have come from other countries, principally the Philippines, Australia, India, and South Africa. Outflow has been at a lower level, mainly to other English-speaking developed countries--Australia, the United States, New Zealand, Ireland, and Canada. The United Kingdom is a net importer of nurses. The principal policy instrument in the United Kingdom, the Code of Practice on International Recruitment, has not ended the inflow of nurses to the United Kingdom from sub-Saharan Africa. CONCLUSIONS: Given the increasing globalization of labor markets, it is likely that the historically high levels of inflow of internationally recruited nurses to the United Kingdom will continue over the next few years; however the "peak" number reached in 2002/2003 may not be repeated, particularly as large-scale active international recruitment has now been ended, for the short term at least. New English language tests and other revised requirements for international applicants being introduced by the Nurses and Midwives Council from September 2005 may restrict successful applications from some countries and will also probably add to the "bottleneck" of international nurse applicants. Demographic-driven demand for health care, combined with a potential reduction in supply of U.K. nurses as many more reach potential retirement age means that international recruitment is likely to remain on the policy agenda in the longer term, even with further growth in the number of home-based nurses being trained.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".