Tobacco 21: An Important Public Policy to Protect Our Youth
Bibliographic record
Abstract
An important approach to reduce youth tobacco use is the adoption of regulations to prohibit tobacco product sale to individuals younger than 21 years, termed Tobacco 21. In the United States, close to 90% of current smokers started smoking before the age of 18 years, and 99% before age 26 years. Earlier age of tobacco use initiation is associated with lower rates of smoking cessation. Increasing minimum age to purchase has been shown to reduce tobacco product use among youth. The critical determinant is likely the loss of social sources of tobacco products. Enforcement activities are important for age-of-purchase laws to be effective. Raising the minimum legal age to purchase tobacco products to 21 years is highly supported among both the smoking and nonsmoking public. Tobacco sales to those younger than 21 years account for just 2% of total tobacco sales, yet produce 90% of new smokers. The short-term effect on small business of raising the minimum age to purchase would be minimal. Small businesses will have time to adapt to the decrease in tobacco sales as fewer youth grow up nicotine addicted. Raising the minimum age to purchase of tobacco and nicotine products to 21 years, combined with enforcement of those restrictions, will help protect future generations from a lifetime of tobacco dependence and associated morbidity. These regulations should apply to all tobacco products, including electronic nicotine delivery systems. Respiratory health care providers should educate their local, state, and federal policy makers on the importance of Tobacco 21.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.037 | 0.031 |
| Insufficient payload (model declined to judge) | 0.030 | 0.022 |
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".