Adolescents' Perceptions of Canadian Cigarette Package Warning Labels: Investigating the Effects of Message Framing
Bibliographic record
Abstract
This study investigates gain-framed and loss-framed messages on graphic cigarette warning labels and their effects on adolescents' smoking-related attitudes and behaviors. Canadian cigarette warning labels emphasizing health consequences of smoking (loss-framed) were digitally manipulated into gain-framed versions. High school students (N = 210) completed a questionnaire measuring attitudes, perceptions of the warnings, and behavioral intentions. The study used a posttest-only comparison group design with random assignment. The independent variable was message framing (loss-framed, gain-framed avoidance, gain-framed benefits), and the dependent variables were (a) attitudes toward the warning, (b) attitudes toward smoking, (c) effectiveness in reducing smoking levels, (d) intentions to smoke, (e) effectiveness in improving one's ability to quit, and (f) effectiveness in increasing the likelihood of a smoker quitting. Results indicate that adolescents had more favorable attitudes toward the loss-framed warnings and perceived them as more effective than the gain-framed warnings. Further, smokers exposed to the loss-framed version featuring decaying teeth had significantly lower intentions to smoke in the future. Loss-framed warning labels appear to have a positive influence on adolescents' smoking-related attitudes and behavioral intentions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".