Beliefs about the relative harm of “light” and “low tar” cigarettes: findings from the International Tobacco Control (ITC) China Survey
Bibliographic record
Abstract
BACKGROUND: Many smokers in Western countries perceive "light" or "low tar" cigarettes as less harmful and less addictive than "regular" or "full flavoured" cigarettes. However, there is little research on whether similar perceptions exist among smokers in low and middle incomes, including China. OBJECTIVE: To characterise beliefs about "light" and "low tar" cigarettes among adult urban smokers in China. METHODS: We analysed data from Wave 1 of the ITC China Survey, a face-to-face household survey of 4732 adult Chinese smokers randomly selected from six cities in China in 2006. Households were sampled using a stratified multistage design. FINDINGS: Half (50.0%) of smokers in our sample reported having ever tried a cigarette described as "light," "mild" or "low tar". The majority of smokers in our sample (71%) believed that "light" and/or "low tar" cigarettes are less harmful compared to "full flavoured" cigarettes. By far the strongest predictor of the belief that "light" and/or "low tar" cigarettes are less harmful was the belief that "light" and/or "low tar" cigarettes feel smoother on the respiratory system (p<0.001, OR=53.87, 95% CI 41.28 to 70.31). CONCLUSION: Misperceptions about "light" and/or "low tar" cigarettes were strongly related to the belief that these cigarettes are smoother on the respiratory system. Future tobacco control policies should go beyond eliminating labelling and marketing that promotes "light" and "low tar" cigarettes by regulation of product characteristics (for example, additives, filter vents) that reinforce perceptions that "light" and "low tar" cigarettes are smoother on the respiratory system and therefore less harmful.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".