Evaluation of smoke-free policies in seven cities in China, 2007-2012
Bibliographic record
Abstract
BACKGROUND: China is the world's largest consumer of tobacco, with hundreds of millions of people exposed daily to secondhand smoke (SHS). Comprehensive smoke-free policies are the only effective way to protect the population from the harms of SHS. China does not have a comprehensive national smoke-free law but some local-level regulations have been implemented. OBJECTIVE: To evaluate local level smoke-free regulations across 7 cities in China by measuring the prevalence of smoking in public places (workplaces, restaurants and bars), and support for smoke-free policies over time. METHODS: Data were from Waves 2 to 4 of the International Tobacco Control (ITC) China Survey (2007-2012), a face-to-face cohort survey of approximately 800 smokers in each of 7 cities in mainland China. Multivariate logistic regression models estimated with generalised estimating equations were used to test the changes in variables over time. RESULTS: As of 2012, over three-quarters of respondents were exposed to smoking in bars; more than two-thirds were exposed to smoking in restaurants and more than half were exposed to smoking in indoor workplaces. Small decreases in the prevalence of smoking were found overall from Waves 2 to 4 for indoor workplaces, restaurants and bars, although the decline was minimal for bars. Support for complete smoking bans increased over time for each venue, although it was lowest for bars. CONCLUSIONS: Existing partial smoking bans across China have had minimal impact on reducing smoking in public places. A strongly enforced, comprehensive national smoke-free law is urgently needed in order to achieve greater public health gains.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".