The Relationship Between Tobacco and Cannabis Use: A Review
Bibliographic record
Abstract
BACKGROUND: Based on the prevalence and health implications of tobacco and cannabis use, aiming to reduce their use, especially among youth, is a sound objective at both the individual and public health level. A proper understanding of the relationships between tobacco and cannabis use may help to achieve this goal. OBJECTIVES: To review the relationships between tobacco and cannabis use. METHODS: A selective review of the literature. RESULTS: We present an overview of the motivations for tobacco and cannabis use, and their perceived harmfulness. The article then reviews the gateway theory, reverse gateway theory, route of administration theory, and common liability theory. We describe the link between co-use and dependence symptoms, and the substitution phenomenon between tobacco and cannabis use. Three forms of simultaneous use-mulling, blunt smoking, and chasing-and their impacts are explained. We summarize the impact of tobacco use on cannabis (and vice versa) treatment outcomes, and, finally, review new treatments that simultaneously target tobacco and cannabis dependence. Most of the literature reviewed here relates to substance use among adolescents and young adults. CONCLUSIONS: The use of tobacco and cannabis-two of the most widely used substances around the world-are strongly intertwined in several respects. Both health professionals and researchers should have well-informed views on this issue to better evaluate, understand, inform, and provide care to their patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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".