Global migration and factors influencing retention of Asian internationally educated nurses: a systematic review
Bibliographic record
Abstract
BACKGROUND: Given nurses' increasing international mobility, Asian internationally educated nurses (IENs) represent a critical human resource highly sought after within the global healthcare workforce. Developed countries have grown excessively reliant on them, leading to heightened competition among these countries. Hence, this review aims to uncover factors underlying the retention of Asian IENs in host countries to facilitate the development of more effective staff retention strategies. METHODS: A mixed-methods systematic review was conducted using the Joanna Briggs Institute methodology for mixed-method systematic review. A search was undertaken across the following electronic databases for studies published in English during 2013-2022: CINAHL, Embase, PubMed, Scopus, Web of Science and PsycINFO. Two of the researchers critically appraised included articles independently using the Joanna Briggs Critical Appraisal Tools and Mixed Methods Appraisal Tool (version 2018). A data-based convergent integrated approach was adopted for data synthesis. RESULTS: Of the 27 included articles (19 qualitative and eight quantitative), five each were conducted in Asia (Japan, Taiwan, Singapore and Malaysia), Australia and Europe (Italy, Norway and the United Kingdom); four each in the United States and the Middle East (Saudi Arabia and Kuwait); two in Canada; and one each in New Zealand and South Africa. Five themes emerged from the data synthesis: (1) desire for better career prospects, (2) occupational downward mobility, (3) inequality in career advancement, (4) acculturation and (5) support system. CONCLUSION: This systematic review investigated the factors influencing AMN retention and identified several promising retention strategies: granting them permanent residency, ensuring transparency in credentialing assessment, providing equal opportunities for career advancement, instituting induction programmes for newly employed Asian IENs, enabling families to be with them and building workplace social support. Retention strategies that embrace the Asian IENs' perspectives and experiences are envisioned to ensure a sustainable nursing workforce.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".