Plain packaging implementation: perceptions of risk and prestige of cigarette brands among Aboriginal and Torres Strait Islander people
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of plain packaging with larger graphic health warnings on perceptions of risk and prestige related to different cigarette brands among Aboriginal and Torres Strait Islander people in the Australian Capital Territory. We hypothesised that the changes would decrease perceptions that 'some cigarette brands are more harmful than others', and that 'some brands are more prestigious than others', and this would be stronger among participants aged ≤35 years, and among smokers compared with non-smokers. METHODS: Participants completed the survey prior to packaging changes, and were followed up 12 months later (n=98). Repeated measures ANCOVAs assessed perception changes. RESULTS: Following plain packaging implementation, there was a significant reduction in perceptions that 'some cigarette brands are more harmful than others'. There was no overall change in perceptions of prestige. However, there was a significant interaction for age. Analyses indicated a reduction in perceptions that 'some cigarette brands are more prestigious than others' among younger participants (p=0.05), but no change among older participants (p>0.20). There was no interaction for smoking status for perceptions of prestige, indicating smokers' and non-smokers' perceptions did not differ on this measure. CONCLUSIONS: These findings provide support for the packaging changes.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".