Support for smoke-free policies among smokers and non-smokers in six cities in China: ITC China Survey
Bibliographic record
Abstract
OBJECTIVE: To examine levels of support for comprehensive smoke-free policies in six large Chinese cities. METHODS: Data from Wave 1 of the International Tobacco Control (ITC) China Survey (April-August 2006) were analysed. The ITC China Survey employed a multistage sampling design in Beijing, Shenyang, Shanghai, Changsha, Guangzhou and Yinchuan (none of which has comprehensive smoke-free policies in place). Face-to-face interviews were conducted with 4815 smokers and 1270 non-smokers. Multivariate logistic regression models were used to identify factors associated with support for comprehensive smoke-free policies. RESULTS: About one in two Chinese urban smokers and four in five non-smokers believed that secondhand smoke (SHS) causes lung cancer. The majority of respondents supported comprehensive smoke-free policies in hospitals, schools and public transport vehicles while support for smoke-free workplaces, restaurants and bars was lower. Levels of support were generally comparable between smokers and non-smokers. Support for comprehensive smoke-free policies was positively associated with knowledge about the harm of SHS. Respondents who worked in a smoke-free worksite or who frequented smoke-free indoor entertainment places were more likely to support comprehensive smoking restriction in bars and restaurants. CONCLUSION: Considerable support for smoke-free policies exists in these six large cities in China. Greater public education about the dangers of SHS may further increase support. Experiencing the benefits of smoke-free indoor entertainment places and/or workplaces increases support for these policies and suggests that some initial smoke-free policy implementation may hasten the diffusion of these public health policies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".