‘When I Migrated, I Faced Challenges but Gained Much More…’ Challenges and Support Needs of Internationally Educated Nurses—A Qualitative Study
Bibliographic record
Abstract
AIM: This study aims to examine the experiences and support needs of internationally educated nurses (IENs) who migrated from Türkiye to different countries. BACKGROUND: With the impact of globalisation and changes in the healthcare sector, the migration of IENs is on the rise. The global shortage of nurses has prompted high-income countries to increase their recruitment of nurses from low- and middle-income countries. METHODS: This descriptive qualitative study included 16 nurses who had migrated to seven different countries: Germany (n = 3), England (n = 3), the USA (n = 3), Canada (n = 2), Sweden (n = 2), Ireland (n = 2) and Switzerland (n = 1). Data were collected between August and November 2024 using Google Meet. The data were analysed using content analysis. The COREQ Checklist was utilised for data analysis and reporting. RESULTS: Content analysis identified four main themes: (1) challenges encountered, (2) professional and personal development gains, (3) support needs and (4) recommendations for development and adaptation. CONCLUSIONS: This study revealed that IENs face challenges such as professional adjustment, language barriers and cultural differences, while also experiencing gains such as professional skill development and enhanced intercultural nursing competencies. IMPLICATIONS FOR NURSING PRACTICE AND POLICIES: The findings highlight the critical role of orientation and mentoring programmes that include language training, cultural awareness and psychological support and emphasise the need for more inclusive and sustainable health policies that support the integration of IENs. REPORTING METHOD: The Consolidated Criteria for Reporting Qualitative Studies (COREQ). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".