Welfare regimes modify the association of disadvantaged adult-life socioeconomic circumstances with self-rated health in old age
Bibliographic record
Abstract
BACKGROUND: Welfare regimes in Europe modify individuals' socioeconomic trajectories over their life-course, and, ultimately, the link between socioeconomic circumstances (SECs) and health. This paper aimed to assess whether the associations between life-course SECs (early-life, young adult-life, middle-age and old-age) and risk of poor self-rated health (SRH) trajectories in old age are modified by welfare regimes (Scandinavian [SC], Bismarckian [BM], Southern European [SE], Eastern European [EE]). METHODS: We used data from the longitudinal SHARE survey. Early-life SECs consisted of four indicators of living conditions at age 10. Young adult-life, middle-age, and old-age SECs indicators were education, main occupation and satisfaction with household income, respectively. The association of life-course SECs with poor SRH trajectories was analysed by confounder-adjusted multilevel logistic regression models stratified by welfare regime. We included 24 011 participants (3626 in SC, 10 256 in BM, 6891 in SE, 3238 in EE) aged 50 to 96 years from 13 European countries. RESULTS: The risk of poor SRH increased gradually with early-life SECs from most advantaged to most disadvantaged. The addition of adult-life SECs differentially attenuated the association of early-life SECs and SRH at older age across regimes: education attenuated the association only in SC and SE regimes and occupation only in SC and BM regimes; satisfaction with household income attenuated the association across regimes. CONCLUSIONS: Early-life SECs have a long-lasting effect on SRH in all welfare regimes. Adult-life SECs attenuated this influence differently across welfare regimes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".