Do Welfare Regimes Moderate Cumulative Dis/advantages Over the Life Course? Cross-National Evidence from Longitudinal SHARE Data
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the cumulative disadvantage of different forms of childhood misfortune and adult-life socioeconomic conditions (SEC) with regard to trajectories and levels of self-rated health in old age and whether these associations differed between welfare regimes (Scandinavian, Bismarckian, Southern European, and Eastern European). METHOD: The study included 24,004 respondents aged 50-96 from the longitudinal SHARE survey. Childhood misfortune included childhood SEC, adverse childhood experiences, and adverse childhood health experiences. Adult-life SEC consisted of education, main occupational position, and financial strain. We analyzed associations with poor self-rated health using confounder-adjusted mixed-effects logistic regression models for the complete sample and stratified by welfare regime. RESULTS: Disadvantaged respondents in terms of childhood misfortune and adult-life SEC had a higher risk of poor self-rated health at age 50. However, differences narrowed with aging between adverse-childhood-health-experiences categories (driven by Southern and Eastern European welfare regimes), categories of education (driven by Bismarckian welfare regime), and main occupational position (driven by Scandinavian welfare regime). DISCUSSION: Our research did not find evidence of cumulative disadvantage with aging in the studied life-course characteristics and age range. Instead, trajectories showed narrowing differences with differing patterns 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 | 0.004 | 0.010 |
| 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 | 0.001 | 0.002 |
| 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".