Tobacco purchasing in Australia during regular tax increases: findings from the International Tobacco Control Policy Evaluation Project
Bibliographic record
Abstract
OBJECTIVE: We examined Australian tobacco purchasing trends, the average self-reported price paid within each purchase type and the association between type of tobacco product purchased and participant characteristics, including quit intentions, between 2007 and 2020. METHODS: =11 534). The main outcome measures were type of tobacco products purchased: RYO, carton, pack or pouch size and brand segment. Logistic regression, fit using generalised estimating equations, was estimated the association between the outcome and participant characteristics. RESULTS: The reported price-minimising purchasing patterns increased from 2007 to 2020: any RYO (23.8-43.9%), large-sized pack (2007: 24.0% to 2016: 34.3%); shifting from large-sized to small-sized packs (2020: 37.7%), and economy brand (2007: 37.2% to 2020: 59.3%); shifting from large (2007: 55.8%) to small economy packs (2014: 15.3% to 2020: 48.1%). Individuals with a lower income, a higher nicotine dependence level and no quit intention were more likely to purchase RYO and large-sized packs. CONCLUSION: RYO, large-sized packs and products with a low upfront cost (eg, small RYO pouches and small-sized economy brand packs) may appeal to people on low incomes. Australia's diverse tobacco pack and pouch sizes allow the tobacco industry to influence tobacco purchases. Standardising pack and pouch sizes may reduce some price-related marketing and especially benefit people who have a low income, are highly addicted and have no quit intention.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".