[Degree of exposure to secondhand smoking and related knowledge, attitude among adults in urban China].
Bibliographic record
Abstract
OBJECTIVE: To identify the levels of exposure to second-hand smoking (SHS) among Chinese adults living in the urban areas and their knowledge on the risks of SHS, to support for the Smoke-free policy. METHODS: Data from the Global Adult Tobacco Survey (GATS) and the International Tobacco Control Policy Evaluation China Survey (ITC China Survey) was analyzed and SAS was used to calculate the rates and 95%CI. RESULTS: In the two surveys, less than 40% of the respondents reported that their workplaces had completely stopped smoking. Participants who reported that they had seen people smoking at various public places with different rates, also they could reflect the levels to SHS exposure. Restaurants were the venue with the heaviest overall exposure (83.4%-95.6%), followed by the workplace (53.3%-84.0%). Exposure was low in health facilities, schools and public transport venues. In the GATS survey, 60.6% smokers and 68.5% non-smokers believed that SHS could cause lung cancer, but only one-third of the participants believed that SHS could cause heart diseases in adults. Participants in the ITC China survey reported a comparatively higher level of awareness on the harm of SHS, but only 58.2% smokers believed that SHS could cause heart diseases in adults. Overall, data from the ITC China survey showed that participants' support for a comprehensive smoke-free policy in schools, health-related facilities, government buildings and in taxi were high (over 70% ). However, the proportion of participants supporting comprehensive smoking-free policy at workplaces (50.9%-60.9%) was relatively low. CONCLUSION: The proportion of indoor workplaces with complete smoking ban was low in urban areas but levels to SHS exposure were high. People's awareness of harms related to SHS and their attitude on setting up a comprehensive smoke-free workplace need to be improved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".