How do consumers perceive differences in risk across nicotine products? A review of relative risk perceptions across smokeless tobacco, e-cigarettes, nicotine replacement therapy and combustible cigarettes
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature regarding relative risk perceptions (RRPs) across non-combustible nicotine products. DATA SOURCES: MEDLINE and PsycINFO databases were searched for articles published up to October 2014. STUDY SELECTION: Of the 5266 records identified, articles not published in English that did not quantitatively assess RRPs across categories of non-combustible nicotine products were excluded, yielding 55 records. DATA EXTRACTION: One reviewer extracted measures and findings of RRPs for product comparisons of smokeless tobacco (SLT), e-cigarettes (ECs) and nicotine replacement therapy (NRT) to one another, and to combustible cigarettes (CCs). DATA SYNTHESIS: A total of 157 samples from 54 studies were included in the analyses. The accuracy of RRPs differed based on the products being compared: although the accuracy of RRPs was variable across studies, substantial proportions of respondents reported inaccurate beliefs about the relative harmfulness of SLT versus CCs, as well as of ECs versus NRT. In addition, in most studies, respondents did not know the relative harmfulness of SLT versus NRT. In contrast, respondents in many studies correctly perceived NRT and ECs as less harmful than CCs. Cigarette smokers and users of non-combustible nicotine products tended to correctly perceive the relative harmfulness of products more often than non-users. Measures used to assess RRPs varied across studies, with different approaches characterised by certain strengths and limitations. CONCLUSIONS: The highly variable and context-specific nature of non-combustible nicotine product RRPs have direct implications for researchers and present several challenges for policymakers working with modified risk products, including issues of measurement, health risk communication and behaviour change.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| 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".