A scoping review on the measurement of transnationalism in migrant health research in high-income countries
Bibliographic record
Abstract
BACKGROUND: Migrants commonly maintain transnational ties as they relocate and settle in a new country. There is a growing body of research examining transnationalism and health. We sought to identify how transnationalism has been defined and operationalized in migrant health research in high income countries and to document which populations and health and well-being outcomes have been studied in relation to this concept. METHODS: We conducted a scoping review using the methodology recommended by the Joanna Briggs Institute (JBI). We searched nine electronic databases; no time restrictions were applied. Studies published in English or French in peer-reviewed journals were considered. Studies were eligible if they included a measure of transnationalism (or one of its dimensions; social, cultural, economic, political and identity ties and/or healthcare use) and examined health or well-being. RESULTS: Forty-seven studies, mainly cross-sectional designs (81%), were included; almost half were conducted in the United States. The majority studied immigrants, broadly defined; 23% included refugees and/or asylum-seekers while 36% included undocumented migrants. Definitions of transnationalism varied according to the focus of the study and just over half provided explicit definitions. Most often, transnationalism was defined in terms of social connections to the home country. Studies and measures mainly focused on contacts and visits with family and remittance sending, and only about one third of studies examined and measured more than two dimensions of transnationalism. The operationalization of transnationalism was not consistent and reliability and validity data, and details on language translation, were limited. Almost half of the studies examined mental health outcomes, such as emotional well-being, or symptoms of depression. Other commonly studied outcomes included self-rated health, life satisfaction and perceived discrimination. CONCLUSION: To enhance comparability in this field, researchers should provide a clear, explicit definition of transnationalism based on the scope of their study, and for its measurement, they should draw from validated items/questions and be consistent in its operationalization across studies. To enhance the quality of findings, more complex approaches for operationalizing transnationalism (e.g., latent variable modelling) and longitudinal designs should be used. Further research examining a range of transnationalism dimensions and health and well-being outcomes, and with a diversity of migrant populations, is also warranted.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".