The Impact of Implementing Tobacco Control Policies: The 2017 Tobacco Control Policy Scorecard
Bibliographic record
Abstract
The Tobacco Control Scorecard, published in 2004, presented estimates of the effectiveness of different policies on smoking rates. Since its publication, new evidence has emerged. We update the Scorecard to include recent studies of demand-reducing tobacco policies for high-income countries. We include cigarette taxes, smoke-free air laws, media campaigns, comprehensive tobacco control programs, marketing bans, health warnings, and cessation treatment policies. To update the 2004 Scorecard, a narrative review was conducted on reviews and studies published after 2000, with additional focus on 3 policies in which previous evidence was limited: tobacco control programs, graphic health warnings, and marketing bans. We consider evaluation studies that measured the effects of policies on smoking behaviors. Based on these findings, we derive estimates of short-term and long-term policy effect sizes. Cigarette taxes, smoke-free air laws, marketing restrictions, and comprehensive tobacco control programs are each found to play important roles in reducing smoking prevalence. Cessation treatment policies and graphic health warnings also reduce smoking and, when combined with policies that increase quit attempts, can improve quit success. The effect sizes are broadly consistent with those previously reported for the 2004 Scorecard but now reflect the larger evidence base evaluating the impact of health warnings and advertising restrictions.
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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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".