Testing Cessation Messages for Cigarette Package Inserts: Findings from a Best/Worst Discrete Choice Experiment
Bibliographic record
Abstract
= 1000) participated in three discrete choice experiments (DCEs): DCE 1 assessed five cessation benefit topics and five imagery types; DCE 2 assessed five messages with tips to improve cessation success and five imagery types; DCE 3 assessed four reproductive health benefits of cessation topics and four imagery types. In each DCE, participants evaluated four or five sets of four inserts, selecting the most and least motivating (DCEs 1 & 3) or helpful (DCE 2) for quitting. Linear mixed models regressed choices on insert and smoker characteristics. For DCE 1, the most motivating messages involved novel disease topics and imagery of younger women. For DCE 2, topics of social support, stress reduction and nicotine replacement therapy were selected as most helpful, with no differences by imagery type. For DCE 3, imagery influenced choices more than topic, with imagery of a family or a mom and baby selected as most motivating. Statistically significant interactions for all three experiments indicated that the influence of imagery type on choices depended on the message topic. Messages to promote smoking cessation through cigarette pack inserts should consider specific combinations of message topic and imagery.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".