Considerations and Future Research Directions for E-Cigarette Warnings—Findings from Expert Interviews
Bibliographic record
Abstract
Tobacco warning labels are important sources of risk information but research historically has been cigarette-centric. This qualitative study aimed to inform future direction and research on warnings for e-cigarettes. Between June and August 2016, we conducted interviews with 10 researchers with expertise in tobacco warning label research. Interviewees were registrants of a 2016 National Cancer Institute grantee meeting on tobacco warnings. Several participants agreed that the Food and Drug Administration's new nicotine addiction warning for e-cigarettes could be informative but that it might not resonate with young people. Many agreed that more than one warning would be important as e-cigarette science evolves and that research on additional warning themes (e.g., nicotine exposure, harmful constituents) and execution styles (including use of pictorials) was important. Participants were somewhat mixed about the use of reduced-risk messages within e-cigarette warnings, but agreed that research on how to communicate about cigarette/e-cigarette relative risks was needed. Overall, more research is needed on tobacco warnings for non-cigarette products, including on the message content, placement, execution and potential impact on audiences' product knowledge, risk perceptions and use intentions. This is particularly needed for products such as e-cigarettes which may have harm-reduction potential relative to cigarettes and require unique considerations.
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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 | 0.135 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".