Identifying best practices in adoption, implementation and enforcement of flavoured tobacco product restrictions and bans: lessons from experts
Bibliographic record
Abstract
OBJECTIVE: To identify recommended components for adopting, implementing and enforcing bans or restrictions targeting flavoured tobacco products. METHODS: Between April and June 2019, semistructured interviews were conducted with 17 high-level experts across the USA and Canada with expertise in flavoured tobacco product policies. Participants included health department staff, researchers, legal professionals and local government officials. Interviews were recorded, transcribed and analysed for key themes. RESULTS: Major findings were organised into four categories: programme planning and legislative preparations; education and community outreach; implementation and enforcement; and policy impact. Critical pre-implementation elements included using comprehensive policy language, identifying enforcement agents, examining potential economic costs, deploying media campaigns and engaging community partners and retailers. Recommended implementation processes included a 6-month preparation timeline, focus on retailer education and clearly outlined enforcement procedures, particularly for concept flavours. CONCLUSIONS: Flavoured tobacco policies have successfully limited sales, withstood legal challenges and become more comprehensive over time, providing useful lessons to inform ongoing and future legislative and programmatic efforts. Identifying and sharing best practices can improve passage, implementation, efficacy and evaluation of flavoured tobacco policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".