The migration and transitioning experiences of internationally educated nurses: a global perspective
Bibliographic record
Abstract
AIM: To comprehensively review recent literature related to the migration and transitioning experiences of internationally educated nurses (IENs). BACKGROUND: Many developed nations are redressing nursing deficits by recruiting IENs. Acquiring credentialing is historically recognized as a barrier to obtaining meaningful employment, yet broader issues of transition into global health care contexts are also significant. METHODS: A database search of CINAHL, Medline, Scopus and Web of Science, and a hand-search of key nursing journals produced 239 combined hits, with 21 articles meeting the inclusion criteria. RESULTS: Five common themes were extracted and synthesized including: (1) reasons for and challenges with immigration, (2) cultural displacement, (3) credentialing difficulties and 'deskilling', (4) discriminatory experiences and (5) strategies of IENs which smoothed transition. CONCLUSIONS: Although major reasons for migration are related to improved income and professional stature, these have overwhelmingly shown to erode upon relocation. Cultural displacement appears to largely stem from communication and language differences, feelings of being an outsider and differences in nursing practice. The deskilling process and discrimination are also key players which hinder transition and demoralize many IENs. IMPLICATIONS FOR NURSING MANAGEMENT: The present study highlights that the huge advantages in professional skill and cultural diversity that IENs can bring to any nursing unit will not be fully realized without substantial efforts to reduce practice limitations (deskilling) and discrimination. Individual strategies for easing the transition should be taught to IENs, probably through mentorship by experienced IENs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".