Effects of Tobacco Taxation and Pricing on Smoking Behavior in High Risk Populations: A Knowledge Synthesis
Bibliographic record
Abstract
Tobacco taxation is an essential component of a comprehensive tobacco control strategy. However, to fully realize the benefits it is vital to understand the impact of increased taxes among high-risk subpopulations. Are they influenced to the same extent as the general population? Do they need additional measures to influence smoking behavior? The objectives of this study were to synthesize the evidence regarding differential effects of taxation and price on smoking in: youth, young adults, persons of low socio-economic status, with dual diagnoses, heavy/long-term smokers, and Aboriginal people. Using a better practices approach, a knowledge synthesis was conducted using a systematic review of the literature and an expert advisory panel. Experts were involved in developing the study plan, discussing findings, developing policy recommendations, and identifying priorities for future research. Most studies found that raising cigarette prices through increased taxes is a highly effective measure for reducing smoking among youth, young adults, and persons of low socioeconomic status. However, there is a striking lack of evidence about the impact of increasing cigarette prices on smoking behavior in heavy/long-term smokers, persons with a dual diagnosis and Aboriginals. Given their high prevalence of smoking, urgent attention is needed to develop effective policies for the six subpopulations reviewed. These findings will be of value to policy-makers and researchers in their efforts to improve the effectiveness of tobacco control measures, especially with subpopulations at most risk. Although specific studies are needed, tobacco taxation is a key policy measure for driving success.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".