Perceived nicotine content of reduced nicotine content cigarettes is a correlate of perceived health risks
Bibliographic record
Abstract
BACKGROUND: Reducing cigarette nicotine content may reduce smoking. Studies suggest that smokers believe that nicotine plays a role in smoking-related morbidity. This may lead smokers to assume that reduced nicotine means reduced risk, and attenuate potential positive effects on smoking behaviour. METHODS: Data came from a multisite randomised trial in which smokers were assigned to use cigarettes varying in nicotine content for 6 weeks. We evaluated associations between perceived and actual nicotine content with perceived health risks using linear regression, and associations between perceived nicotine content and perceived health risks with smoking outcomes using linear and logistic regression. FINDINGS: =1.66, 95% CI 0.87 to 2.44) perceived greater health risks. Nevertheless, individuals perceiving low (OR=0.48, 95% CI 0.32 to 0.71) or moderate nicotine (OR=0.42, 95% CI 0.27 to 0.66) were less likely than those perceiving very low nicotine to report that they would quit within 1 year if only investigational cigarettes were available. Lower perceived risk of developing other cancers and heart disease was also associated with fewer cigarettes/day at week 6. CONCLUSIONS: Although the perception of reduced nicotine is associated with a reduction in perceived harm, it may not attenuate the anticipated beneficial effects on smoking behaviour. These findings have implications for potential product standards targeting nicotine and highlight the need to clarify the persistent harms of reduced nicotine combusted tobacco products.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".