Reasons for Supporting or Opposing a Reduced Nicotine Product Standard
Bibliographic record
Abstract
Objectives: In 2017, the US Food and Drug Administration (FDA) announced a potential new product standard lowering nicotine in cigarettes to minimally or non-addictive levels. Understanding why the public supports or opposes this standard could inform messaging efforts. Methods: We collected online survey data in 2017 from 2508 respondents. We coded and analyzed the open-ended text responses describing reasons for support or opposition among those who strongly agreed (39.9% of sample) and strongly disagreed (11.4%) with the proposed nicotine standard. Results: The most common reasons for opposition were viewing the new standard as a threat to personal freedom and believing that it would lead themselves or others to smoke more. The most common reasons for support were believing the standard would help themselves or others quit smoking and recognizing the harms of smoking and nicotine. Some responses reflected inaccurate understanding of nicotine's effects, and some themes (eg, believing the standard could prevent addiction) were more common among smokers than nonsmokers. Conclusions: Findings could inform public health campaign messages from the FDA and other agencies by building on existing reasons for support (eg, would help with cessation) and counteracting inaccurate beliefs (eg, would make people smoke more).
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".