Smokers’ awareness of filter ventilation, and how they believe it affects them: findings from the ITC Four Country Survey
Bibliographic record
Abstract
BACKGROUND: Filter ventilation creates sensations of 'lightness' or 'smoothness' and is also highly effective for controlling machine-tested yields of tar, nicotine and carbon monoxide. Nearly all factory-made cigarettes (FMC) now have filter ventilation in countries such as Australia, Canada, the UK and the USA. Research conducted before 'light' and 'mild' labelling was banned found low smoker awareness of filter ventilation and its effects. This study explores current levels of awareness of filter ventilation and current understanding of its effects in these four countries. METHODS: We used data from the 2018 wave of the ITC Four Country Smoking and Vaping Survey with samples from USA, England, Canada and Australia. Analyses were conducted initially on a weighted sample of 11 844, and subsequently on 7541 daily FMC smokers. FINDINGS: Only 40.3% of all respondents reported being aware of filter ventilation. Among daily FMC smokers, only 9.4% believed their cigarettes had filter ventilation. Believing that their usual cigarettes are smoother was positively associated with believing they are also less harmful. Both these beliefs independently predict believing their cigarettes are ventilated (smoother OR=1.97 (95% CI 1.50 to 2.59) and less harmful OR=2.41 (95% CI 1.66 to 3.49) in relation to those believing each characteristic is average. INTERPRETATION: Awareness of filter ventilation is currently low, despite decades of public 'education efforts around the misleading nature of 'light' and 'mild" descriptors. Few smokers realise that their cigarettes almost certainly are vented. Smokers who believed their cigarettes have filter ventilation were more likely to believe they were both smoother and less harmful. Awareness of the technology appears to be insufficient to prevent smokers being deceived by it. Filter ventilation is inherently misleading to smokers and it is time to ban it.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".