Ethnic disparity and exposure to supplements rather than adverse childhood experiences linked to preterm birth in Pakistani women
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) are associated with prenatal mental health and negative pregnancy outcomes in high income countries, but whether the same association exists in Pakistan, a low- to middle-income (LMI) country, remains unclear. METHODS: Secondary data analyses of a prospective longitudinal cohort study examining biopsychosocial measures of 300 pregnant women at four sites in Karachi, Pakistan. A predictive multiple logistic regression model for preterm birth (PTB; i.e., <37 weeks' gestation) was developed from variables significantly (P < 0.05) or marginally (P < 0.10) associated with PTB in the bivariate analyses. RESULTS: Of the 300 women, 263 (88%) returned for delivery and were included in the current analyses. The PTB rate was 11.1%. We found no association between ACE and PTB. Mother's education (P = 0.011), mother's ethnicity (P = 0.010), medications during pregnancy (P = 0.006), age at birth of first child or current age if primiparous (P = 0.049) and age at marriage (P = 0.091) emerged as significant in bivariate analyses. Mother's ethnicity and taking medications remained predictive of PTB in the multivariate model. LIMITATIONS: Findings are limited by the relatively small sample size which precludes direct testing for possible interactive effects. CONCLUSIONS: In sum, pathways to PTB for women in LMI countries may differ from those observed in high-income countries and may need to be modelled differently to include behavioural response to emotional distress and socio-cultural contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".