A cross-sectional study on levels of secondhand smoke in restaurants and bars in five cities in China
Bibliographic record
Abstract
OBJECTIVES: To assess indoor secondhand smoke (SHS) exposure in restaurants and bars via PM(2.5) level measurements in five cities in China. METHODS: The study was conducted from July to September in 2007 in Beijing, Xi'an, Wuhan, Kunming and Guiyang. PM(2.5) concentrations were measured in 404 restaurants and bars using portable aerosol monitors. The occupant density and the active smoker density were calculated for each venue sampled. RESULTS: Among the 404 surveyed venues, 23 had complete smoking bans, nine had partial smoking bans and 313 (77.5%) had smoking observed during sampling. The geometric mean of indoor PM(2.5) levels in venues with smoking observed was 208 μg/m(3) and 99 μg/m(3) in venues without smoking observed. When outdoor PM(2.5) levels were adjusted, indoor PM(2.5) levels in venues with smoking observed were consistently significantly higher than those in venues without smoking observed (F=80.49, p<0.001). Indoor PM(2.5) levels were positively correlated with outdoor PM(2.5) levels (partial ρ=0.37 p<0.001) and active smoker density (partial ρ=0.34, p<0.001). CONCLUSIONS: Consistent with findings in other countries, PM(2.5) levels in smoking places are significantly higher than those in smoke-free places and are strongly related to the number and density of active smokers. These findings document the high levels of SHS in hospitality venues in China and point to the urgent need for comprehensive smoke-free laws in China to protect the public from SHS hazards, as called for in Article 8 of the Framework Convention on Tobacco Control, which was ratified by China in 2005.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".