Combating the tobacco epidemic in North America: challenges and opportunities
Bibliographic record
Abstract
According to the WHO, the Region of the Americas has the second lowest tobacco use prevalence of any WHO region.1 WHO projections based on trends since 2000 indicate that the Region of the Americas, which includes both North and South America, is the only region expected to achieve a 30% relative reduction in tobacco use by 2025.1 However, there are approximately 127 million persons who report smoking tobacco in the Americas Region,2 a majority of whom reside in North America.3 North America consists of 23 countries with a combined population of nearly 600 million people, or approximately 7.5% of the world’s population in 2019.4 Among North American countries, data from 2017 for persons aged 15 years or older show current tobacco smoking prevalence ranged from 6.0% in Panama to 27.8% in Cuba.5 Among students aged 13–15 years old in North American countries with available data through 2017, current tobacco smoking prevalence ranged from 4.4% in Dominican Republic to 18.1% in Mexico.5 Tobacco smoking among adults is higher among males than females across North America. However, the difference in prevalence between sexes in the Region of the Americas is among the lowest of any WHO region5; this pattern is particularly pronounced among youth, where tobacco smoking among girls is similar to or higher among boys in most countries.2 5 Despite lower tobacco use relative to other regions, and future projected reductions,1 challenges remain to combating the tobacco epidemic in North America, including diversification of the product landscape, tobacco industry interference and uneven application of evidence-based strategies. This commentary discusses these challenges, as well as opportunities for future action to reduce the burden of tobacco use in North America. Over the past decade and a half, the tobacco and nicotine product landscape has evolved …
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".