The Effect of Welfare State Policy Spending on the Equalization of Socioeconomic Status Disparities in Mental Health
Bibliographic record
Abstract
This article examines whether and how the relationship between socioeconomic status (SES) and depression is modified by welfare state spending using the 2006, 2012, and 2014 survey rounds of the European Social Survey (ESS) merged with macroeconomic data from the World Bank, Eurostat, and SOCX database (N = 87,466). Welfare state spending effort divided between social investment and social protection spending modifies the classic inverse relationship between SES and depression. Distinguishing policy areas in both social investment and social protection spending demonstrates that policy programs devoted to education, early childhood education and care, active labor market policies, old age care, and incapacity account for differences in the effect of SES across countries. Our analysis finds that social investment policies better explain cross-national differences in the effect of SES on depression, implying policies focused earlier in the life course matter more for understanding social disparities in the mental health of populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".