Impact of the 'Giving Cigarettes is Giving Harm' campaign on knowledge and attitudes of Chinese smokers
Bibliographic record
Abstract
OBJECTIVE: To date there is limited published evidence on the efficacy of tobacco control mass media campaigns in China. This study aimed to evaluate the impact of a mass media campaign 'Giving Cigarettes is Giving Harm' (GCGH) on Chinese smokers' knowledge of smoking-related harms and attitudes towards cigarette gifts. METHODS: Population-based, representative data were analysed from a longitudinal cohort of 3709 adult smokers who participated in the International Tobacco Control (ITC) China Survey conducted in six Chinese cities before and after the campaign. Logistic regression models were estimated to examine associations between campaign exposure and attitudes towards cigarette gifts measured post-campaign. Poisson regression models were estimated to assess the effects of campaign exposure on post-campaign knowledge, adjusting for pre-campaign knowledge. FINDINGS: Fourteen percent (n=335) of participants recalled the campaign within the cities where the GCGH campaign was implemented. Participants in the intervention cities who recalled the campaign were more likely to disagree that cigarettes are good gifts (71% vs 58%, p<0.01) and had greater levels of campaign-targeted knowledge than those who did not recall the campaign (mean=1.97 vs 1.62, p<0.01). Disagreeing that cigarettes are good gifts was higher in intervention cities than in control cities. Changes in campaign-targeted knowledge were similar in both cities, perhaps due to a secular trend, low campaign recall or contamination issues. CONCLUSIONS: These findings suggest that the GCGH campaign increased knowledge of smoking harms, which could promote downstream cessation. This study provides evidence to support future campaign development to effectively fight the tobacco epidemic in China.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".