Association Between Secondhand Smoke Exposure Among Women and the Implementation of Tobacco Control Measures on Campus: A Cross-Sectional Study in 50 Universities Across China
Bibliographic record
Abstract
INTRODUCTION: Exposure to secondhand smoke (SHS) among women is prevalent in China which increases their risk of developing a wide range of diseases and can affect their susceptibility to adverse reproductive health effects. This study aims to examine the association between SHS exposure among women and the adoption and implementation of tobacco control measures on campus in China. AIMS AND METHODS: 7469 female college students who have never smoked were recruited from 50 universities across China using a multistage sampling technique. All participants reported their exposure to SHS and the tobacco advertising and promotion on campus. Participants from colleges with smoke-free policies reported the implementation of smoke-free policies on campus measured by: (1) no evidence of smoking and (2) the display of smoke-free signs in public places. Multivariate logistic regression models were applied using weighted survey data. RESULTS: SHS exposure among participants was 50.5% (95% CI = 44.2% to 56.9%). The adoption of a smoke-free policy was not associated with SHS exposure (OR: 1.01, 95% CI = .71, 1.42), however, the implementation of the policy was significantly negatively associated with SHS exposure (OR: 0.56, 95% CI = .47 to 0.67). In addition, tobacco advertising and promotion on campus were significantly positively associated with SHS exposure (OR: 2.33, 95% CI = 1.42, 3.82; OR: 1.52; 95% CI = 1.15, 2.02, respectively). CONCLUSIONS: Exposure to SHS is prevalent among female college students in China. Successful implementation of a smoke-free policy and banning tobacco advertising and promotion on campus could be effective measures to protect young women from the harms of SHS in China. IMPLICATIONS: Approximately half of female college students are exposed to SHS on campus in China. Failure to implement smoke-free policies and exposure to tobacco marketing on campus are associated with higher SHS exposure. To protect millions of young Chinese women from the health harms of SHS, universities need to enact and enforce smoke-free policies within campus boundaries and adopt comprehensive bans on tobacco advertising and promotion on campus.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".