Structural gender inequalities and symptoms of postpartum depression in 40 countries
Bibliographic record
Abstract
Introduction The extent to which structural gender inequality contributes to macro-level differences in postpartum depression (PPD) remains largely unknown. Objectives To examine the association of structural gender inequalities with national-level prevalence estimates of PPD symptoms. Methods Meta-analytically derived national-level estimates for the prevalence of PPD symptoms – based on the Edinburgh Postnatal Depression Scale (EPDS) – were combined with economic (e.g., income inequality), health (e.g., infant mortality rate), sociodemographic (e.g., urban population), and structural gender inequality variables (e.g., abortion policies) for 40 countries (276 primary studies). Data came from a prior meta-analysis on PPD prevalence and international agencies (e.g., UNICEF). Meta-regression techniques and traditional p-value based stepwise procedures, complemented with a Bayesian model averaging approach, were used for a robust selection of variables associated with national-level PPD symptom prevalence. Sensitivity analyses excluded primary studies with small sample sizes or countries lacking evidence for psychometric properties of the EPDS. Results Income inequality (β = 0.04, 95% CI = 0.02 to 0.07) and abortion policies (β = 0.02, 95% CI = 0.00 to 0.03) were the only variables included in the final, adjusted model, accounting for 60.7% of the variance in PPD symptoms across countries. Gradual liberalizations of abortion policies were associated with a 2% decrease in national-level PPD symptom prevalence. Results were robust to sensitivity analyses. Conclusions Structural gender inequalities might be social determinants of PPD, as the liberalization of abortion policies seem to impact women’s perinatal mental health on a population level. More research on structural gender inequality is needed to guide policy and practice. Disclosure No significant relationships.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".