Development of a tobacco 21 policy assessment tool and state-level analysis in the USA, 2015–2019
Bibliographic record
Abstract
OBJECTIVE: Policies raising the minimum legal sales age (MLSA) of tobacco products to 21 are commonly referred to as tobacco 21. This study sought to identify components of tobacco 21 policies and develop an instrument to examine policy language within 16 state laws adopted by July 2019. METHODS: The multistage tool development process began with a review of established literature and existing tobacco 21 policies. In a series of meetings, tobacco control experts identified key policy components used to develop an initial tool. After testing and revisions, the instrument was used to code the existing tobacco 21 state-level policies. Inter-rater reliability (κ=0.70) was measured and discrepancies were discussed until consensus was met. Policy component frequencies were reported by state. RESULTS: While all 16 states raised the MLSA to 21, the laws varied widely. Two laws omitted purchaser identification requirements. Fifteen laws mentioned enforcement would include inspections, but only three provided justification for conducting inspections. All 16 states provided a penalty structure for retailer/clerk violations, but penalties ranged considerably. Fourteen states required a tobacco retail licence, nine renewed annually. Six laws contained a military exemption, five were phased-in and 10 contained purchase, use or possession laws, which penalised youth. Four states introduced or expanded pre-emption of local tobacco control. CONCLUSIONS: The instrument developed is the first to examine policy components within state-level tobacco 21 laws. Policies that include negative components or omit positive components may not effectively prevent retailers from selling to youth, which could result in less effective laws.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".