Borders that hurt: the link between anti-immigration attitudes in Europe and the epidemic of chronic pain in immigrant adolescents
Bibliographic record
Abstract
ABSTRACT: Global immigration is increasing and is expected to continue rising because of the growing frequency of environmental disasters and sociopolitical conflicts. Emerging research shows that adolescents who immigrated to Europe are disproportionately affected by chronic pain compared to their nonimmigrant peers. Anti-immigration attitudes, which are also rising in Europe, may contribute to this disparity by fostering social exclusion and discrimination, which can heighten psychological distress and contribute to chronic pain. Using an intersectional approach, this study examined (1) the association between country-level anti-immigration attitudes and chronic pain in immigrant adolescents and (2) whether this association differed by sex and socioeconomic status. Cross-sectional data were drawn from 5621 immigrant adolescents across 20 European countries using the Health Behaviour in School-aged Children 2018 survey. Country-level anti-immigration attitudes were obtained from the European Social Survey 2018. Weighted mixed-effects logistic regression models assessed the association between anti-immigration attitudes and chronic pain and tested for interaction effects with sex and socioeconomic status. Results showed that stronger country-level anti-immigration attitudes were significantly associated with a higher prevalence of chronic pain among immigrant adolescents (OR = 1.17; 95% CI = 1.11-1.38) and that this association was stronger in immigrants with low socioeconomic status (OR = 1.39; 95% CI = 1.08-1.77). Sociopolitical exclusion of immigrants may contribute to higher chronic pain rates in immigrant adolescents, especially those with lower socioeconomic status. Country-specific responses must acknowledge the public health burden of anti-immigration rhetoric. Findings underscore the need for structural interventions to reduce anti-immigration sentiments at societal and political levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".