Noticing cigarette health warnings and support for new health warnings among non-smokers in China: findings from the International Tobacco Control project (ITC) China survey
Bibliographic record
Abstract
BACKGROUND: Health warnings labels (HWLs) have the potential to effectively communicate the health risks of smoking to smokers and non-smokers, and encourage smokers to quit. This study sought to examine whether non-smokers in China notice the current text-only HWLs and whether they support adding more health information and including pictures on HWLs. METHODS: Adult non-smokers (n = 1324) were drawn from Wave 4 (September 2011-November 2012) of the International Tobacco Control (ITC) China Survey. The proportion of non-smokers who noticed the HWLs, and supported adding more health information and pictures to the HWLs was examined. Additionally, the relation between non-smokers' demographic characteristics, including whether they had a smoking partner, their number of smoking friends, and noticing the HWLs and support for adding health information and pictures was examined. Because the HWLs changed during the survey period (April 2012), differences between non-smokers who completed the survey before and after the change were examined. RESULTS: 12.2% reported they noticed the HWLs often in the last month. The multivariate model, adjusting for demographics showed that respondents with a smoking partner (OR = 2.41, 95% CI 1.42-4.13, p = 0.001) noticed the HWLs more often. 64.8% of respondents agreed that the HWLs should have more information, and 80.2% supported including pictures. The multivariate model showed that non-smokers who completed the survey after the HWLs were implemented (OR = 0.63, 95% CI 0.40-0.99, p = 0.04) were less likely to support adding more health information. The multivariate model showed a significant relation between having a smoking partner and supporting pictorial HWLs (OR = 2.03, 95% CI 1.24-3.33, p = 0.005). CONCLUSIONS: The findings indicate that the Chinese HWLs are noticed by a minority of non-smokers and that non-smokers strongly support strengthening the Chinese warning labels with more health information and pictures. Additionally, because the HWLs are noticed more often by non-smokers with a smoking spouse/partner, HWLs could be used to communicate the dangers of smoking and secondhand smoke exposure to non-smokers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".