State-level tobacco environments and sexual orientation disparities in tobacco use and dependence in the USA
Bibliographic record
Abstract
OBJECTIVE: To describe relationships between tobacco-related environments and disparities in smoking by sexual orientation. METHODS: We examined three aspects of state-level tobacco environments, which were derived from the ImpacTeen State Level Tobacco Control Policy and Prevalence Database: (1) tobacco price and tax data and tobacco control funding; (2) tobacco control policies and (3) tobacco prevalence and norms data. This information was linked to individual-level data on sexual orientation, tobacco use and nicotine dependence in Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (N=34 653; 577 LGB respondents), a cross-sectional, nationally representative survey of adults in the USA. RESULTS: Lesbian, gay and bisexual (LGB) adults in states with more restrictive tobacco environments were less likely to have ever smoked (AOR=0.78, 95% CI 0.62 to 1.00) and to currently smoke (AOR=0.77, 95% CI 0.60 to 0.99) than LGB adults in more permissive tobacco environments. Further, sexual orientation disparities in past and current smoking, as well as in current nicotine dependence, were lower in states with the most restrictive tobacco environments. Results were robust to adjustment for confounders at the individual and state levels. CONCLUSIONS: Restrictive state-level tobacco environments are correlates of smoking behaviours among LGB adults in the USA; such environments could potentially reduce social inequalities in smoking based on sexual orientation.
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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 | 0.001 |
| 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".