[Tobacco use in the region of the Americas: elements for a program of action].
Bibliographic record
Abstract
Tobacco consumption is one of the most important public health challenges faced in the Americas. This is not only due to the great number of deaths attributable to smoking, many of which are premature, but also to the high economic and social costs of medical care and the burden of disease and disability imposed by tobacco consumption on health systems and on the population. In the regional epidemiologic situation. South American countries are characterized by the highest consumption rates, followed by the Andean region and Mexico; Central American and Caribbean countries have the lowest smoking prevalences. Only the United States and Canada have been able to hold back the smoking epidemic; the rest of the hemisphere shows stable or increasing smoking rates. In the region, age of smoking initiation has decreased and the number of women who smoke has increased. This article reviews the current tobacco control measures in Latin American legislations and analyzes selected regional characteristics such as the structure of young populations, control measures that are weak or scarce, and the world production of tobacco. There is a compelling need to establish economic, population-based, and legislative procedures leading to a gradual reduction of the current tobacco consumption rates. This paper advances a comprehensive action plan against tobacco consumption.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".