Migration and Women's Health Research (2000−2023): A bibliometric analysis of trends and gaps
Bibliographic record
Abstract
This bibliometric study examines the scholarly landscape of migration and women's health, analyzing 1314 Scopus-indexed articles from 462 journals published between 2000 and 2023. Findings indicate a consistent increase in research output, reflecting growing global interest in this interdisciplinary field. Geographically, high-income countries (HICs), including the United States, Canada, the United Kingdom, and Australia, dominate contributions, while low- and middle-income countries (LMICs) remain underrepresented despite hosting significant migrant populations. International collaborations play a crucial role, with key institutions such as the University of California and the London School of Hygiene and Tropical Medicine shaping research efforts. The keyword co-occurrence analysis highlights migration, gender dynamics, mental health, and reproductive health as dominant themes. Persistent gaps in mental and reproductive healthcare access for migrant women emphasize the need for trauma-informed care (TIC), mobile bilingual healthcare services, and inclusive health policies. Disparities in research funding further exacerbate global health inequities, underscoring the necessity of equitable redistribution of resources, including redirecting at least 10 % of HIC research grants to LMIC-led studies. The COVID-19 pandemic magnified pre-existing vulnerabilities, stressing the importance of multilateral collaborations and sustainable policy interventions to enhance migrant healthcare access. This study provides valuable insights into research trends, collaboration networks, and thematic focus areas, offering a foundation for future interdisciplinary research and evidence-based policymaking aimed at promoting health equity for migrant women globally.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.212 | 0.402 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".