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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".