Evaluation of strategies to communicate harmful and potentially harmful constituent (HPHC) information through cigarette package inserts: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: The US Food and Drug Administration (FDA) has regulatory authority to use inserts to communicate with consumers about harmful and potentially harmful constituents (HPHCs) in tobacco products; however, little is known about the most effective manner for presenting HPHC information. METHODS: In a discrete choice experiment, participants evaluated eight choice sets, each of which showed two cigarette packages from four different brands and tar levels (high vs low), accompanied by an insert that included between-subject manipulations (ie, listing of HPHCs vs grouping by disease outcome and numeric values ascribed to HPHCs vs no numbers) and within-subject manipulations (ie, 1 of 4 warning topics; statement linking an HPHC with disease vs statement with no HPHC link). For each choice set, participants were asked: (1) which package is more harmful and (2) which motivates them to not smoke; each with a 'no difference' option. Alternative-specific logit models regressed choice on attribute levels. RESULTS: 1212 participants were recruited from an online consumer panel (725 18-29-year-old smokers and susceptible non-smokers and 487 30-64-year-old smokers). Participants were more likely to endorse high-tar products as more harmful than low-tar products, with a greater effect when numeric HPHC information was present. Compared with a simple warning statement, the statement linking HPHCs with disease encouraged quit motivation. CONCLUSIONS: Numeric HPHC information on inserts appears to produce misunderstandings that some cigarettes are less harmful than others. Furthermore, brief narratives that link HPHCs to smoking-related disease may promote cessation versus communications that do not explicitly link HPHCs to disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".