Influence of Warning Statements on Understanding of the Negative Health Consequences of Smoking
Bibliographic record
Abstract
INTRODUCTION: Pursuant to the Tobacco Control Act (TCA), the US Food and Drug Administration (FDA) is developing new cigarette health warnings to convey the negative health consequences of cigarette smoking. AIMS AND METHODS: This study assessed which of 15 revised warning statements (10 on topics similar to TCA statements and 5 on other topics) promoted greater understanding of cigarette smoking risks relative to TCA statements. In February 2018, adolescent and adult smokers and adolescents susceptible to smoking (n = 2505) completed an online experiment. Control condition participants viewed TCA statements; treatment condition participants viewed combinations of TCA and revised statements. Analyses compared revised statements to TCA statements on the same health topic or to randomly selected TCA statements if there were no statements on the same topic. RESULTS: Relative to TCA statements, 12 of 15 revised statements were more likely to be considered new information, and 12 resulted in more self-reported learning. Three revised statements made participants think more about health risks than TCA statements; the reverse was true for one revised statement. Participants rated most TCA and revised statements as moderately believable and informative. Seven revised statements were found to be less believable and factual, and one revised statement more believable and factual. Treatment condition participants correctly selected more smoking-related health conditions than control condition participants (13.79 versus 12.42 of 25). CONCLUSIONS: Findings suggest that revised statements can promote greater understanding of cigarette smoking risks. Results informed FDA's selection of warning text that was paired with images for testing in a follow-up study. IMPLICATIONS: The US FDA may adjust the text of the cigarette warning statements provided in the TCA if the revised statements promote greater public understanding of the negative health consequences of cigarette smoking. Most of the revised warning statements tested were more likely to be considered new information and resulted in more self-reported learning compared with paired TCA statements, providing support for using revised statements as part of cigarette health warnings. These results informed the development of pictorial cigarette warnings by FDA that were tested in a follow-up study and included in a proposed rule.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".