The impact of social determinants of health on international migrants’ health outcomes: a bibliometric analysis
Bibliographic record
Abstract
Purpose The aim of this study is to conduct an in-depth exploration of the research landscape concerning the impact of social determinants of health (SDH) on the health outcomes of international migrants. Design/methodology/approach Leveraging the extensive Scopus database, this study retrieved a total of 2,255 articles spanning the years 1993–2023. The framework for analysis used the SDH categories outlined by the World Health Organization. Findings The research landscape exhibited an apparent increase in the number of publications, but not a net increase in the research productivity. The USA emerged as the leading contributor to research output, with the Journal of Immigrant and Minority Health emerging as the most prolific publication venue, and the University of Toronto ranking as the most prolific institution. The SDH category that received the highest number of publications was the “community and social context”. Migrants from different regions in Asia (East, Central and South Asia) and those from Latin America and the Caribbean region appeared to be the most commonly researched. Highly cited articles predominantly delved into mental health outcomes arising from discrimination and migration policies. Research limitations/implications The findings proffer valuable insights for shaping future research endeavors, accentuating the imperative for diversified studies encompassing underrepresented domains, broader health outcomes and the inclusion of migrant populations from different world regions in investigative pursuits. Originality/value This study delivers a comprehensive analysis of the research landscape, unveiling critical trends in the realm of SDH and migrant health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.163 | 0.177 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".