Who smokes in smoke-free public places in China? Findings from a 21 city survey
Bibliographic record
Abstract
Efforts toward controlling secondhand smoke in public places have been made throughout China. However, in contrast to the western world, significant challenges remain for effectively implementing smoke-free regulations. This study explores individual and regional factors which influence smoking in smoke-free public places. Participants included 16 866 urban residents, who were identified through multi-stage sampling conducted in 21 Chinese cities. The reported smoking prevalence in smoke-free public places was 41.2%. Of those who smoked in smoke-free public places, 45.9% had been advised to stop smoking. Participants stated that no-smoking warnings/signs with 'please' in the statement had a better likelihood of gaining compliance and preventing smoking in public spaces. Multilevel logistic regression analysis showed that ethnicity, education, occupation, type of smoking, age of smoking initiation, smoking situation, stress, household smoking restrictions and city population were all associated with smoking in smoke-free public places. Interestingly local smoke-free regulations were not associated with smoking in public places. The findings underscore that efforts to restrict smoking in public places in China should emphasize strong enforcement, while simultaneously raising public awareness of the perils of second hand smoke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".