Alcohol and tobacco use among preconception women in India
Bibliographic record
Abstract
Background Most of the women who use alcohol and tobacco before pregnancy likely to use alcohol and tobacco during pregnancy. The objectives of this study was to describe alcohol and tobacco use, and to identify associated characteristics during preconception period.Methods The Indian National Family Health Survey-4 was used. A total of 65,238 preconception, young, married women were included and analysed usingbivariate and multivariate analyses statistical techniques.Results The data revealed that 4.3% of preconception women in India were using alcohol and tobacco. The prevalence of alcohol and tobacco use was higher among older women (5.6%), Scheduled Tribes (14.2%), living in rural areas (5.0%), non-educated (8.9%), poorest households (9.1%), northeast region (25.0%) and with a low body mass index (BMI) (6.2%). The adjusted odds ratios shown that after controlling for important background factors, being Hindu, from scheduled tribes, already having children, being underweight, from poorest households and Northeast region had higher odds of alcohol and tobacco use compared to with their corresponding reference groups.Conclusion Findings suggest that preconception women of reproductive age who use alcohol and tobacco should be strongly encouraged and supported to quit alcohol and tobacco use before pregnancy for preventing adverse birth outcomes.
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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 | 0.000 |
| 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".