Bibliometric analysis of global migration health research in peer-reviewed literature (2000–2016)
Bibliographic record
Abstract
BACKGROUND: The health of migrants has become an important issue in global health and foreign policy. Assessing the current status of research activity and identifying gaps in global migration health (GMH) is an important step in mapping the evidence-base and on advocating health needs of migrants and mobile populations. The aim of this study was to analyze globally published peer-reviewed literature in GMH. METHODS: A bibliometric analysis methodology was used. The Scopus database was used to retrieve documents in peer-reviewed journals in GMH for the study period from 2000 to 2016. A group of experts in GMH developed the needed keywords and validated the final search strategy. RESULTS: The number of retrieved documents was 21,457. Approximately one third (6878; 32.1%) of the retrieved documents were published in the last three years of the study period. In total, 5451 (25.4%) documents were about refugees and asylum seekers, while 1328 (6.2%) were about migrant workers, 440 (2.1%) were about international students, 679 (3.2%) were about victims of human trafficking/smuggling, 26 (0.1%) were about patients' mobility across international borders, and the remaining documents were about unspecified categories of migrants. The majority of the retrieved documents (10,086; 47.0%) were in psychosocial and mental health domain, while 2945 (13.7%) documents were in infectious diseases, 6819 (31.8%) documents were in health policy and systems, 2759 (12.8%) documents were in maternal and reproductive health, and 1918 (8.9%) were in non-communicable diseases. The contribution of authors and institutions in Asian countries, Latin America, Africa, Middle East, and Eastern European countries was low. Literature in GMH represents the perspectives of high-income migrant destination countries. CONCLUSION: Our heat map of research output shows that despite the ever-growing prominence of human mobility across the globe, and Sustainable Development Goals of leaving no one behind, research output on migrants' health is not consistent with the global migration pattern. A stronger evidence base is needed to enable authorities to make evidence-informed decisions on migration health policy and practice. Research collaboration and networks should be encouraged to prioritize research in GMH.
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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 | 0.035 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.327 | 0.364 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".