Comparative study of major depressive symptoms among pregnant women by employment status
Bibliographic record
Abstract
The objectives of our study were to compare the prevalence of major depressive symptoms between subgroups of pregnant women: working women, women who had stopped working, housewives and students; and to identify risk factors for major depressive symptoms during pregnancy. The CES-D scale (Center for Epidemiological Studies Depression scale) was used to measure major depressive symptoms (CES-D score ≥23) in 5337 pregnant women interviewed at 24-26 weeks of pregnancy. Multivariate logistic regression models were developed to identify risk factors associated with major depressive symptoms. Prevalence of major depressive symptoms was 11.9% (11.0-12.8%) for all pregnant women. Working women had the lowest proportion of major depressive symptoms [7.6% (6.6-8.7%); n = 2514] compared to housewives [19.1% (16.5-21.8%); n = 893], women who had stopped working [14.4% (12.7-16.1%); n = 1665], and students [14.3% (10.3-19.1%); n = 265]. After adjusting for major risk factors, the association between pregnant women's employment status and major depressive symptoms remained significant for women who had stopped working (OR: 1.61; 95% CI 1.26 to 2.04) and for housewives (OR: 1.46; 95% CI 1.10 to 1.94), but not for students (OR: 1.37; 95% CI 0.87 to 2.16). In multivariate analyses, low education, low social support outside of work, having experienced acute stressful events, lack of money for basic needs, experiencing marital strain, having a chronic health problem, country of birth, and smoking were significantly associated with major depressive symptoms. Health professionals should consider the employment status of pregnant women when they evaluate risk profiles. Prevention, detection and intervention measures are needed to reduce the prevalence of prenatal depression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".