Impact and Effectiveness of Legislative Smoking Bans and Anti-Tobacco Media Campaigns in Reducing Smoking among Women in the US: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The purpose of this study is to systematically review the literature addressing the effectiveness of legislative smoking bans and anti-tobacco media campaigns in reducing smoking among women. Methods: MEDLINE, PubMed, CINAHL, and ABI/INFORM were searched for studies published from 2005 onwards. Meta-analysis was conducted using a random effects model and subgroup analysis on pre-selected characteristics. Results: In total, 652 articles were identified, and five studies satisfied the inclusion criteria. The studies varied from school-based to workplace settings and had a total of 800,573 women participants, aged 12 to 64 years old. Three studies used legislative bans, one study used anti-tobacco campaigns and another one used both as their intervention. The overall pooled effect of the five studies yielded an odds ratio (OR) = 1.137 (C.I. = 0.976–1.298 and I2 = 85.6%). Subgroup analysis by intervention revealed a significant pooled estimate for studies using legislative smoking bans OR = 1.280 (C.I. = 1.172–1.389 and I2 = 0%). Conclusion: Legislative smoking bans were found to be associated with a reduction in the smoking rates among women compared to anti-tobacco media campaigns. Further research in this area is needed.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".