Evaluating Cigarette Pack Insert Messages with Tips to Quit
Bibliographic record
Abstract
Objectives: Canada is the only country that currently uses cigarette pack inserts to communicate health messages to smokers, including tips to quit. Messages about strategies for quitting smoking are also central to the US Food and Drug Administration's (FDA) Every Try Counts (ETC) campaign. This study assessed US smokers' responses to Canadian and ETC-based messages formatted for pack inserts. Methods: US adult smokers (N = 524) were recruited from an online consumer panel and rated 8 insert messages: 4 based on Canadian inserts and 4 based on ETC. Participants randomly viewed each message accompanied by an image of either a person or a symbolic representation of the topic. Participants rated the perceived effectiveness (PE) of each message. Paired t-tests were used to assess mean differences in PE across topics, image types, and quit intentions. Results: ETC messages were consistently rated as more effective than Canadian messages regardless of quit intentions. Image types did not significantly influence PE. Conclusions: Messages from ETC are perceived as more effective than messages used in Canada. The FDA has the authority to communicate with smokers through inserts and should consider adopting inserts to promote smoking cessation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".