Internationally Educated Nurses Experience of the First Two Years Working and Living in England:a mixed methods study
Bibliographic record
Abstract
The recruitment of international nurses in England is increasing to fill nursing vacancies and meet the rising demands of healthcare. Progressing recruitment to the oversight of the retention of England’s nursing workforce is a transactional and brittle ineffective plan for both individual international nurses and healthcare systems alike. Migrating to live and work thousands of kilometres away from home, often imposing separation from partners and families, for an undetermined period of time is a significant decision for an international nurse to take (Bond, 2022). Despite the large numbers of international nurses migrating to live and work in England in recent years, there are no studies published that explore their lived experiences during the initial few years’ post migration (Dahl et al., 2022; Palmer et al. 2021). This study therefore builds on the global knowledge of international nurses’ motivations for migration and explores their experiences in the first two years postmigration in England with the intention of laying a foundation of new knowledge in this currently under-investigated phenomenon (Pressley et al., 2022; Buchan et al., 2022).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".