Smokers' and e-cigarette users' perceptions of modified risk warnings for e-cigarettes
Bibliographic record
Abstract
The 2009 Family Smoking Prevention and Tobacco Control Act opened the possibility for tobacco companies to apply to market their products as having "modified" or reduced risks. However, research on how to communicate comparative tobacco risks and how such messages are interpreted is limited. This study aimed to qualitatively examine perceptions of potential modified risk statements presented as warning labels for e-cigarettes. We conducted six focus groups between 2014 and 2015 with 27 adult e-cigarette users and cigarette-only smokers who provided comments on two versions of a modified risk warning for e-cigarettes: 1) "WARNING: No tobacco product is safe, but this product presents substantially lower risks to health than cigarettes" (as proposed by two companies for their smokeless tobacco products) and 2) "WARNING: This product may be harmful to health, but is substantially less harmful than cigarettes" (an alternative developed by our team). Although most personally believed that e-cigarettes are safer than cigarettes and some thought the messages were true and accurate, many were skeptical and uncomfortable with the warnings because they did not "seem like a warning" and because use of the phrase "substantially lower risks" could be misleading and difficult to understand. Several thought the second warning was stronger (e.g., more active, more specific). Modified risk messages about e-cigarettes may impact perceptions and use of the product. More research is needed to identify the framing, wording and placement (e.g. within or in addition to a warning) that could potentially increase population-level benefits and minimize harms.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".