International research hotspots and trend on immigrant health from 2017 to 2022
Bibliographic record
Abstract
BackgroundImmigrant health is an key node in achieving the goal of universal health coverage proposed by 2030 Agenda for Sustainable Development. The shock of severe public health emergency may exacerbate the original health-related contradictions of this group and bring more negative health effects. ObjectiveTo identify academic research hotspots and directions of immigrant health, and to examine trends in research hotspots before and after the public health emergency, so as to provide references to study immigrant health and response to public health emergency in the future. MethodsArticles (document type) included in Web of Science core collection from 2017-01-01 to 2022-12-31 were retrieved. Microsoft Excel 2019 was used for descriptive analysis of included papers. VOSviewer 1.6.19 and CiteSpace 6.1.R6 were combined to draw cooperation maps of authors, institutions, and countries/areas to understand their cooperation and communication, and to draw keywords co-occurrence map, keywords clusters map, and keywords burst map to examine the hotspots and trends of immigrant health research before and after public health emergency. ResultsA total of 5997 papers pertaining to immigrant health from 2017 to 2022 were included, and the number of publications every year were overall on the rise generating a group of productive core authors. Institutions from the United States, Canada, and Northern Europe not only tightly cooperated within their countries/areas, but also cooperated frequently among countries/areas, forming an international cooperation network with the United States as its core. The keywords co-occurrence map showed that from 2017 to 2022, the research hotspots of immigrant health mainly focused on target groups of women, children, and refugees, and the study topics of mental health, acculturation, and care. The results of cluster analysis and further extraction found that the research topics in this field were divided into five categories: maternal and child health care, acculturation and mental health, health services, health equity, and chronic disease. The keywords burst map revealed that the research hotspots shifted from ethnic group, risk behavior, and sexes to COVID-19, health equity, social isolation, and victimization under the impact of public health emergency. ConclusionThe cooperation and communication among study teams, institutions, and countries/areas have promoted the development of immigrant health study. Public health emergency has exacerbated the existing vulnerability of immigrants, and the topics related to health equity and social isolation of immigrants have raised attention becoming the research forefront. It is suggested that under the impact of public health emergency, corresponding public health policies are needed to mitigate health inequities and social support is also required for immigrants to ensure their physical and mental health.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.060 | 0.074 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".