Impact of vaping restrictions in public places on smoking and vaping in the United States—evidence using a difference‐in‐differences approach
Bibliographic record
Abstract
AIMS: To estimate whether and to what extent extending indoor smoking restrictions to include electronic cigarettes (ECs) impact the use of ECs and cigarette smoking among adults in the United States. DESIGN: Observational study using a linear probability model and applying a difference-in-differences analysis. SETTING: United States. PARTICIPANTS: People aged 18-54 who lived in US counties where comprehensive indoor smoking laws in bars, restaurants and private work-places have been in place prior to 2010 (n = 45 111 for EC use analysis, n = 75 959 for cigarette use analysis). MEASUREMENTS: Data on cigarette smoking, use of ECs and place of residence from the Tobacco Use Supplement of the Current Population Survey (TUS-CPS 2010-11, 2014-15 and 2018-19) were combined with the American Nonsmokers' Rights Foundation (ANRF) database of state and local indoor smoking and vaping restriction laws. FINDINGS: Indoor vaping restriction (IVR) coverage was not significantly associated with the likelihood of adult EC use [coefficient estimate = 0.001; 95% confidence interval (CI) = -0.009, 0.013, P-value = 0.783]. In addition, IVR coverage was not significantly associated with adult cigarette smoking (coefficient estimate = -0.00; 95% CI = -0.016, 0.015, P-value = 0.954). The non-significant results appeared in different socio-demographic subgroups. CONCLUSIONS: IVRs do not appear to decrease electronic cigarette use among US adults. There is no evidence that IVRs increase or decrease cigarette smoking among US adults.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".