Bibliometric analysis of global research findings on refugee mental health (1992-2022)
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to examine the thirty-year effectiveness and trend of research on refugee mental health. METHODS: A bibliometric analysis methodology was used. Web of Science (WOS) database was used to obtain the necessary data. In particular, a review and analysis encompassed the quantity of publications featuring articles on refugee mental health, the most prolific countries and institutions, the highly cited articles, citation patterns, international collaboration, and the relevant journals. The study's timeframe was defined from 1992 to 2022. RESULTS: The number of documents obtained is 3912. The majority of the documents obtained were in the field of psychiatry. The quantity of publications and citations experienced a notable upsurge, particularly following the year 2016. The United States emerged as the leading country in terms of both the highest number of publications and citations on this subject. The institutions with the highest publication rates are, in order, the University of New South Wales in Australia, the University of Melbourne in Australia, and McGill University in Canada. This bibliometric study shows that publications on refugee mental health have been observed since 1992 and are gaining momentum, especially after 2016. In addition to the terms "refugees" and " mental health," the keywords "depression," " Post-traumatic stress disorder (PTSD)," and "children" were most commonly used. CONCLUSION: Refugee communities also appear to have similar mental illnesses and experiences regardless of where and when they settled in the world. Research collaboration and networks should be encouraged to prioritize research in refugee mental health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.030 | 0.162 |
| 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) | 0.001 | 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".