HEALTH ACROSS BORDERS: A CROSS-NATIONAL COMPARISON OF IMMIGRANT HEALTH IN EUROPE
Bibliographic record
Abstract
Abstract Although older immigrants are a growing share of the total population in many countries, evidence regarding health differentials by nativity in older adulthood remains underdeveloped. We examine whether foreign-born adults 50 and older in Europe are disadvantaged in terms of multiple health domains, what drives the potential immigrant health disadvantage, and whether such differences are contextually dependent or a general feature of the immigrant experience in Europe. We use the Survey of Health, Aging and Retirement in Europe (SHARE) to estimate physical, mental, and social health of middle age and older adults by nativity in 19 countries. We examine whether nativity-based health disparities can be attributed to demographic composition, socioeconomic factors, family and social support, and life course timing of migration. Last, we examine regional differences in nativity-based health disparities. We find that immigrants aged 50 and above in Europe are more likely to report fair/poor physical health, score worse on EURO-D depression scale, and are more likely to be lonely than the native-born. Socioeconomic status and age at migration partially explain these health differences, although immigrant health disparities remain after accounting for these and other factors. We document some contextual variation within Europe. Immigrants in Eastern, Western and Northern Europe are disadvantaged compared to native-born adults in those regions, while immigrants in Southern Europe are in comparable health to their native-born peers. This article offers new insights into the ways that aging immigrant populations will reshape older adult health profiles in a diverse array of countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".