Childhood adversities and gender inequality in mental health: Do gendered institutions matter?
Bibliographic record
Abstract
Rich sociological literature invokes both individual life course and macro-institutional perspectives to explain gender disparities in depression. While life course theories suggest that women’s mental health is more vulnerable to adverse childhood socioeconomic status (SES), institutional theories highlight macro-level drivers of gender disparities. Integrating these perspectives, I argue that female-friendly macro-institutional contexts ameliorate gendered health disparities by reducing the gendered effects of childhood SES on adult depression. Empirically, I employ data from the European Social Survey, which is the largest cross-nationally comparable dataset containing rich information on childhood experience and health. These data thus allow me to decompose the two-way association between gender, childhood SES, and depression across countries with varying gender regimes. The empirical evidence is largely consistent with my intervention. Women experience more pernicious mental health effects from adverse childhood SES than men, but this gender inequality, and the overall harmfulness of childhood adversity among women, is smaller in countries with more female-friendly gender regimes. I conclude by implicating these findings in broader debates about the interplay between childhood SES, gender, institutional contexts, and mental health.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".