Welfare State and Health Disparity Among Immigrants and Native-born in the US and Canada
Bibliographic record
Abstract
The welfare state regime is often generalized across labor market and healthcare policy areas in the study of welfare states and health. This study separates labor market and healthcare welfare state benefits to better understand the effect of healthcare welfare on self-perceived health. Also, immigrant health has been a blindspot for the study of the effect of the welfare state on health, especially in the cross-national context. This study uses Canadian Community Health Survey and National Health Interview Survey to study the overarching question: whether social determinants of health outlined by the fundamental cause of disease theory, education, employment status, and household income are associated with perceived health in the same way for Canadians (liberal labor market welfare with universal healthcare) and Americans (liberal labor market welfare and healthcare). Then, the study looks at whether the association between social determinants of health affects native-born and immigrants in the same way or differently and how Canadian immigrants stack up against American immigrants. The study shows that the theory of the fundamental cause of disease holds even in Canada, where universal healthcare exists. However, the income health gradient is not as steep for Canadian immigrants than for US immigrants, suggesting that income matters less for the health of Canadians than Americans. Immigrant groups of both countries see smaller income-related health inequalities than their native-born population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".