Individual and collective contribution of antenatal psychosocial distress conditions and preterm birth in Pakistani women
Bibliographic record
Abstract
BACKGROUND: We determined whether dimensions of psychosocial distress during pregnancy individually and collectively predicted preterm birth (PTB) in Pakistani women as it may be misleading to extrapolate results from literature predominantly conducted in high-income countries. METHODS: This cohort study included 1603 women recruited from four Aga Khan Hospital for Women and Children in Sindh, Pakistan. The primary binary outcome of PTB (i.e., livebirth before 37 completed weeks' gestation) was regressed on self-reported symptoms of anxiety (Pregnancy-Related Anxiety (PRA) Scale and Spielberger State-Trait Anxiety Inventory Form Y-1), depression (Edinburgh Perinatal Depression Scale (EPDS)), and covariates such as chronic stress (Perceived Stress Scale) assessed with standardized question and scales with established language equivalency (Sindhi and Urdu). RESULTS: All 1603 births occurred between 24 and 43 completed weeks' gestation. PRA was a stronger predictor of PTB than other types of antenatal psychosocial distress conditions. Chronic stress had no effect on the strength of association between PRA and PTB and a slight but non-significant effect on depression. A planned pregnancy significantly lowered risk of PTB among women who experienced PRA. Aggregate antenatal psychosocial distress did not improve model prediction over PRA. CONCLUSIONS: Like studies in high-income countries, PRA became a strong predictor of PTB when considering interactive effects of whether the current pregnancy was planned. Women's resilience and abilities to make sexual and reproductive health decisions are important to integrate in future research. Findings should be generalized with caution as socio-cultural context is a likely effect modifier. We did not consider protective/strength-oriented factors, such as resilience among women.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".