Challenges affecting migrant healthcare workers while adjusting to new healthcare environments: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Shifting demographics, an aging population, and increased healthcare needs contribute to the global healthcare worker shortage. Migrant Health Care Workers (MHCWs) are crucial contributors to reducing this shortage by moving from low-and middle-income countries (LMICs) to high-income countries (HICs) for better opportunities. Economic factors and health workforce demand drive their migration, but they also face challenges adapting to a new country and new working environments. To effectively address these challenges, it is crucial to establish evidence-based policies. Failure to do so may result in the departure of Migrant Healthcare Workers (MHCWs) from host countries, thereby worsening the shortage of healthcare workers. AIM: To review and synthesize the barriers experienced by MHCWs as they adjust to a new country and their new foreign working environments. METHODOLOGY: We followed the PRISMA guidelines and conducted a search in the PubMed and Embase databases. We included cross-sectional studies published after the year 2000, addressing MHCWs from LMIC countries migrating to high-income countries, and published in English. We established a data extraction tool and used the Appraisal tool for Cross-Sectional Studies (AXIS) to assess article quality based on predetermined categories. RESULTS: Through a targeted search, we identified fourteen articles. These articles covered 11,025 MHCWS from low- to medium-income countries, focusing on Europe, the USA, Canada, Australia, New Zealand, and Israel. Participants and respondents' rates were diverse ranging from 12% to 90%. Studies encompassed various healthcare roles and age ranges, mainly 25-45 years, with a significant female presence. Participants resided in host countries for 3-10 years on average. Results are categorized based on the Riverside Acculturation Stress Inventory (RASI) and expanded to include bureaucratic and employment barriers, Gender differences, Natives vs. non-natives, and orientation programs. CONCLUSIONS: The findings emphasize the importance of cultural competence training and tailored support for MHCWs integration and job satisfaction. Time spent in the new healthcare setting and the influence of orientation programs are key factors in shaping their intentions to stay or leave. Despite limitations, these studies provide valuable insights, emphasizing the ongoing need for holistic strategies to facilitate successful integration, ultimately benefiting healthcare systems and well-being for all stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".