Comorbid Anxiety and Depression among Pregnant Pakistani Women: Higher Rates, Different Vulnerability Characteristics, and the Role of Perceived Stress
Bibliographic record
Abstract
Anxiety and depression commonly co-occur during pregnancy and may increase risk of poor birth outcomes including preterm birth and low birth weight. Our understanding of rates, patterns, and predictors of comorbid anxiety and depression is hindered given the dearth of literature, particularly in low- and middle-income (LMI) countries. The aim of this study is (1) to explore the prevalence and patterns of comorbid antenatal anxiety and depressive symptoms in the mild-to-severe and moderate-to-severe categories among women in a LMI country like Pakistan and (2) to understand the risk factors for comorbid anxiety and depressive symptoms. Using a prospective cohort design, a diverse sample of 300 pregnant women from four centers of Aga Khan Hospital for Women and Children in Pakistan were enrolled in the study. Comorbid anxiety and depression during pregnancy were high and numerous factors predicted increased likelihood of comorbidity, including: (1) High level of perceived stress at any time point, (2) having 3 or more previous children, and (3) having one or more adverse childhood experiences. These risks were increased if the husband was employed in the private sector. Early identification and treatment of mental health comorbidities may contribute to decreased adverse birth outcomes in LMI countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".