Implementing a smoke-free generation policy for Canada: estimates of the long-term impacts
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to assess the potential impacts of the introduction of a smoke-free generation (SFG) policy in Canada with a perpetual ban on cigarette sales to anyone born after 2009 instigated on 1 January 2025. METHODS: An existing Canadian model relating to smoking cessation was adapted and augmented to assess the impact of an SFG policy on quality-adjusted life years (QALYs), life expectancy, health care costs, smoking-related taxes, and Canadian tobacco industry gross domestic product (GDP). The cumulative impact of the policy for the entire Canadian population was assessed for time horizons up to 90 years with an annual discount rate of 1.5%. RESULTS: After 50 years, this SFG policy would lead to 476 814 more QALYs, $2.3 billion less in health care costs, $7.4 billion less in smoking-related taxes and a $3.1 billion reduction in tobacco industry GDP. The combined value of health benefits gained and health care costs averted would exceed the sum of tax revenues foregone and reduced GDP, if the value of a QALY was at least $17 147. Use of higher discount rates and inclusion of unrelated health care costs had little impact on the interpretation of the results. CONCLUSION: The implementation of an SFG policy will bring substantive health benefits to the population in Canada. Although health care cost savings are lower than the combination of lost tax revenues and the decline in the GDP from the Canadian tobacco industry, the value of the health benefits realized outweigh the negative offsets.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".