The Moderating Role of State-Level Policy on Individual-Level Social Inequalities in Mental Health: A Cross-National Perspective
Bibliographic record
Abstract
Little is known about how contexts of social policy impact individual-level mental health associations. I utilize rounds 3, 6, 7 of the cross-national European Social Survey to address this gap in the literature. Chapter 2 focuses on how welfare state spending effort, divided between social investment and social protection spending, modifies the classic inverse relationship between SES and depression. Distinguishing policy areas within 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. On balance, our analysis finds that social investment policies better explain cross-national differences in the effect of SES on depression. Chapter 3 asks: Do mothers living in welfare states with greater spending on family policy enjoy better mental health? And, if so, what components of family policy account for these between-country differences? The effects of family policy spending on depression are assessed for single and partnered mothers compared to partnered fathers across total, cash transfers, leave and early child education and care (ECEC) spending. Results suggest that some policies, such as total and leave spending, have significant effects on buffering the effects of motherhood on mental health. Finally, Chapter 4 addresses the gender gap in depression. The gender gap in depression has resisted simple explanation, in part because of changing theories about this gap (Gove and Tudor 1973; Leupp 2017), and also because of gendered expressions of mental health netting out to no difference in mental health overall (Kessler et al. 2005). Following constrained choice theory and role theory at the individual level, the structural determinants of family policy and “flexitime” are tested independently and in combination to assess whether either policy or which policy better explains the gender gap in depression. Findings suggest flexitime policies primarily explain the gender gap in depression, a finding not previously reported, supporting the further use of constrained choice theory to explain gender differences in mental health.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".