Understanding Inequalities of Maternal Smoking—Bridging the Gap with Adapted Intervention Strategies
Bibliographic record
Abstract
Women who are generally part of socially disadvantaged and economically marginalized groups are especially susceptible to smoking during pregnancy but smoking rates are underreported in both research and interventions. While there is evidence to support the short-term efficacy of nicotine replacement therapy (NRT) use in pregnancy, long-term abstinence rates are modest. Current health strategies and interventions designed to diminish smoking in pregnancy have adopted a simplified approach to maternal smoking-one that suggests that they have a similar degree of choice to non-pregnant smokers regarding the avoidance of risk factors, and overlooks individual predictors of non-adherence. As a result, interventions have been ineffective among this high-risk group. For this reason, this paper addresses the multiple and interacting determinants that must be considered when developing and implementing effective strategies that lead to successful smoking cessation: socioeconomic status (SES), nicotine dependence, social support, culture, mental health, and health services. Based on our review of the literature, we conclude that tailoring cessation programs for pregnant smokers may ultimately optimize NRT efficacy and reduce the prevalence of maternal smoking.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".