A Bibliometric Analysis of Refugee Health Publications in the Nursing Field by Visual Mapping Method
Bibliographic record
Abstract
Introduction Millions of people worldwide are forced to migrate to another country and nurses are the key professional for providing necessary health care to this population. Providing nursing care to refugees or immigrants requires diverse transcultural professional competencies based on standardized guidelines. Objectives This study was aimed to examine the bibliographic characteristics of quantitative studies conducted on refugees in the nursing field. Methods The data were obtained from articles scanned in the Web of Science Core Collection database. The 1672 articles that were published between 1980-and 2023 and met the inclusion criteria were analyzed using VOSviewer and Microsoft 365 Excel software. The PRISMA 2020 Checklist was used for reporting. Results Most publications were made in 2020. The United Kingdom, the United States, Canada, and Australia have the highest number of publications, citations, and international cooperation. Additionally, “mental health” is one of the most used keywords in the studies. Conclusions The findings show the importance of empowering nurses working in this field, especially in determining the needs related to mental health services for refugees. The increased migration rates and the growing need for refugee health care highlighted the importance of investment in nursing research within this field. Nurses and researchers should aim to establish partnerships and share best practices with the leading countries. Furthermore, nurses require specialized training to competently evaluate and provide nursing care and mental health services to this vulnerable population. Policymakers must prioritize international collaboration, equitable healthcare, and the integration of mental health services within healthcare systems to improve refugee health and reduce barriers between them and health services. Disclosure of Interest None Declared
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.026 | 0.171 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".