Support for nicotine reduction in cigarettes: findings from the 2016 and 2020 ITC Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
INTRODUCTION: The USA and New Zealand have sought to establish a product standard to set a maximum nicotine level for cigarettes to reduce their addictiveness. This study examined support for very low nicotine cigarettes (VLNCs) in Australia, Canada, England and the USA between 2016 and 2020. METHODS: Repeated cross-sectional data were analysed from participants who currently smoke, formerly smoked or vaped and/or currently vape in the 2016 (n=11 150) and/or 2020 (n=5432) International Tobacco Control (ITC) Four Country Smoking and Vaping Survey. Respondents were asked if they would support a law that reduces the amount of nicotine in cigarettes to make them less addictive. Adjusted and weighted logistic regression analyses estimated the prevalence and predictors of support, such as country, age, sex, education, income, race and smoking/vaping status for VLNCs (support vs oppose/do not know). RESULTS: A majority of respondents supported a VLNC law, with support highest in Canada (69%; 2016 and 2020 combined), followed by England (61%), Australia (60%) and the USA (58%). Overall, support decreased from 62% in 2016 to 59% in 2020 (p=0.004), which did not differ by country. Levels of support differed by smoking/vaping status, where those who exclusively smoked daily showed the lowest level of support (59%) and those who exclusively vaped non-daily had the highest level of support (72%). CONCLUSION: More than half of respondents in all four countries-including those who smoked daily-supported a hypothetical VLNC standard to render cigarettes less addictive. It is important to examine if support is sustained after policies are implemented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".