How do smokers respond to pictorial and threatening tobacco warnings? The role of threat level, repeated exposure, type of packs and warning size
Bibliographic record
Abstract
Purpose This study aims to investigate the role of two structural factors – threat level depicted on fear messages and warning size – as well as two contextual factors – repeated exposure and type of packs – on pictorial and threatening tobacco warnings’ effectiveness. Design/methodology/approach A two (warning threat level: moderate vs high) × two (coverage: 40 vs 75 per cent) × two (packaging type: plain vs branded) within-subjects experiment was carried out. Subjects were exposed three times to pictorial and threatening tobacco warnings. Both self-report and psychophysiological measurements of emotion were used. Findings Results indicate that threat level is the most effective structural factor to influence smokers’ reactions, while warning size has very low impact. Furthermore, emotional arousal, fear and disgust, as well as attitude toward tobacco brand, decrease after the second exposure to pictorial and threatening tobacco warnings, but stay stable at the third exposure. However, there is no effect of repetition on the emotional valence component, arousal-subjective component, on intention of quitting or of reducing cigarette consumption. Finally, there is a negative effect of plain packs on attitude toward tobacco brand over repeated exposures, but there is no effect of the type of packs on smokers’ emotions and intentions. Social implications Useful marketing social guidance, which might help government decision-makers increase the effectiveness of smoking reduction measures, is offered. Originality/value For the first time in this context, psychophysiological and self-report measurements were combined to measure smokers’ reactions toward pictorial and threatening tobacco warnings in a repeated exposure study.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".