Do larger pictorial health warnings diminish the need for plain packaging of cigarettes?
Bibliographic record
Abstract
AIMS: To assess the effects on brand appeal of plain packaging and size of pictorial health warnings (PHWs). DESIGN: Three (30%, 70% and 100% size front-of-pack PHWs) by two (branded versus plain) between-subjects online experiment. SETTING: Australia. PARTICIPANTS: A total of 1203 adult smokers. MEASUREMENTS: Rating of cigarette brands, smoking attitudes and intentions, purchase intent. FINDINGS: Compared to branded packs, plain packs reduced smokers' ratings of 'positive pack characteristics' (P < 0.001), 'positive smoker characteristics' (P < 0.001) and 'positive taste characteristics' (P = 0.039). Plain packs were rated as being smoked by people who were more 'boring' than those who smoked branded packs (P = 0.001). By contrast, increasing size of PHW above 30% only reduced ratings of 'positive pack characteristics' (P = 0.001), but also decreased ratings of smokers as being 'boring' (P = 0.027). Plainness and size of PHW interacted in predicting ratings of 'positive pack characteristics' (P = 0.008), so that when packs were plain, increasing the size of PHW above 30% did not further reduce ratings. Presentation of only plain packs increased the likelihood that smokers would not choose to purchase any pack (20.3%) compared to presentation of only branded packs (15.3%) (odds ratio = 1.4; P = 0.026), while size of PHWs had no influence upon purchase choice. CONCLUSIONS: Plain packaging probably plays a superior role in undermining brand appeal and purchase intent to increasing health warning size. Policymakers should not rely solely upon large health warnings, which are designed primarily to inform consumers about smoking harms, to also reduce brand appeal: both strategies are likely to be required.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".