Environmental Influences on Tobacco Use: Evidence from Societal and Community Influences on Tobacco Use and Dependence
Bibliographic record
Abstract
There is little doubt that nicotine addiction sustains tobacco use in most people and that individual variation in response to tobacco has a strong biological basis. However, the great diversity in tobacco use behaviors observed between countries and within countries over time suggests that biology alone cannot fully explain these variations. This review examines the role of the social environment in understanding tobacco use behaviors and efforts to curb tobacco use at the population level. We conclude that the social environment plays a critical role in determining how innate biological factors involved in nicotine dependency actually get expressed at the population level. Tobacco use as reflected in population trends is seen as the product of the interaction of agent, host, and environmental factors. Government policies are seen as an important modifiable environmental influence that can alter how tobacco products are designed and marketed (agent factors) and how consumers perceive the risks and benefits of smoking (host factors). Evidence suggests that synergy is gained when tobacco control interventions directed at agent, host, and environmental factors are implemented together.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".