Racial/Ethnic Differences in Light of 100% Smoke-free State Laws: Evidence from Adults in the United States
Why this work is in the frame
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Bibliographic record
Abstract
This study estimates racial/ethnic differences in the association between 100% smoke-free state laws and smoking, as well as self-reported health, to facilitate policy aimed at reducing disparities. Data pertain to adults aged 18 years and older, obtained from the public-use Behavioral Risk Factor Surveillance System (2002-2014). The authors exploit variation in the timing of 100% smoke-free state laws using a difference-in-differences model. Examining heterogeneity across racial/ethnic minority groups, the authors consider the association between smoke-free laws and the probability of being: a daily smoker (versus occasional); an occasional smoker (versus former); and at the top of the self-reported health scale (versus the bottom). The authors find that 100% smoke-free state laws were not correlated with smoking among women. Moreover, racial/ethnic minority men who smoked occasionally were less likely to quit than white men, and results suggest that smoke-free laws did not reduce these disparities. However, there is evidence that smoke-free laws reduced the probability of being a daily smoker for Asian and Hispanic/Latinx men, but not the probability of quitting or being at the top of the self-reported health scale. More generally, smoke-free laws were not associated with self-reported health, except that there may have been an improvement among nonsmoking American Indian/Alaska Native women. These findings underscore the importance of looking beyond average effects to consider how 100% smoke-free state laws impact racial/ethnic minorities. There is evidence that they reduced smoking and improved self-reported health for some groups, but a suite of tobacco control policies is necessary to reduce racial/ethnic disparities more broadly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it