Point-of-sale cigarette pricing strategies and young adult smokers’ intention to purchase cigarettes: an online experiment
Bibliographic record
Abstract
Background Point-of-sale tobacco marketing has been shown to be related to tobacco use behaviours; however, specific influences of cigarette price discounts, price tiers and pack/carton availability on cigarette purchasing intention are less understood by the tobacco control community. Methods We conducted discrete choice experiments among an online sample of US young adult smokers (aged 18–30 years; n=1823). Participants were presented scenarios depicting their presence at a tobacco retail outlet with varying availability of cigarette price discounts, price tiers and pack/carton. At each scenario, participants were asked whether they would purchase cigarettes. Generalised linear regression models were used to examine the associations between of cigarette price discounts, price tiers and pack/carton with intention to purchase cigarettes overall and stratified by educational attainment. Results Participants chose to purchase cigarettes in 70.9% of the scenarios. Offering price discounts were associated with higher odds of choosing to purchase cigarettes. Reducing the number of cigarette price tiers available in the store was associated with lower odds of choosing to purchase cigarettes. Stratified analysis showed that offering discounts on high-tier cigarette packs increased odds of choosing to purchase cigarettes among young adult smokers with at least some college education, while offering discounts on medium-tier cigarette packs increased odds of choosing to purchase cigarettes among those with some college education or less (eg, with a 10% discount, adjusted odds ratio [AOR] some college =1.62, 95% confidence interval [CI] 1.21 to 2.16; AOR ≤high school =1.44, 95% CI 1.08 to 1.93). Conclusions Availability of cigarette price discounts, price tiers and pack/carton could potentially influence cigarette purchasing behaviours among young adult smokers. Regulating these marketing strategies may, therefore, reduce education-related smoking disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".