Bridging borders in healthcare: A bibliometric insight into migration, health, and cultural awareness
Bibliographic record
Abstract
This study aims to analyse studies conducted on the migration, health, and cultural awareness triangle and published in the Web of Science (WoS) database using bibliometric methods. 405 open-access research articles published between 2014 and 2024, using the specified keywords, were identified and examined. Analyses were conducted using the Bibliometrix (4.3.0) package and the Biblioshiny tool in R Studio. There is a continuous increase in the number of studies on the subject. Indeed, an average increase of 17.63% has been observed since 2014. The affiliations producing the most articles were the University of Copenhagen, Denmark (34), the University of Amsterdam, Netherlands (22), and McGill University, Canada (21). The findings indicate that migrant and refugee health is increasingly prominent on the global health agenda, with cultural awareness, health literacy, and service delivery becoming central themes. The most central and relevant concept in the studies included in the analysis is "cultural competence." Strong matches were found around this theme with the themes "qualitative research," "migrants," and "health communication." The second most concentrated focus is "migration," with matches found with the themes "refugee," "mental health," and "primary care." Consequently, the relatively small number of studies conducted in immigrant-receiving countries like Turkey is striking. This suggests that directly affected countries need to contribute more strongly to the global academic literature. Increasing interdisciplinary research at the national level will contribute to the transformation of healthcare services into a more inclusive and culturally sensitive framework for immigrants.
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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 | 0.030 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.236 | 0.325 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".