Tobacco use and secondhand smoke exposure during pregnancy in low‐ and middle‐income countries: the need for social and cultural research
Bibliographic record
Abstract
Tobacco use is a leading cause of death and of poor pregnancy outcome in many countries. While tobacco use is decreasing in many high-income countries, it is increasing in many low- and middle-income countries (LMICs), where by the year 2030, 80% of deaths caused by tobacco use are expected to occur. In many LMICs, few women smoke tobacco, but strong evidence indicates this is changing; increased tobacco smoking by pregnant women will worsen pregnancy outcomes, especially in resource-poor settings, and threatens to undermine or reverse hard-won gains in maternal and child health. To date, little research has focused on preventing pregnant women's tobacco use and secondhand smoke (SHS) exposure in LMICs. Research on social and cultural influences on pregnant women's tobacco use will greatly facilitate the design and implementation of effective prevention programs and policies, including the adaptation of successful strategies used in high-income countries. This paper describes pregnant women's tobacco use and SHS exposure and the social and cultural influences on pregnant women's tobacco exposure; it also presents a research agenda put forward by an international workgroup convened to make recommendations in this area.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".