Cannabis use and other predictors of the onset of daily cigarette use in young men: what matters most? Results from a longitudinal study
Bibliographic record
Abstract
BACKGROUND: According to the gateway hypothesis, tobacco use is a gateway of cannabis use. However, there is increasing evidence that cannabis use also predicts the progression of tobacco use (reverse gateway hypothesis). Unfortunately, the importance of cannabis use compared to other predictors of tobacco use is less clear. The aim of this study was to examine which variables, in addition to cannabis use, best predict the onset of daily cigarette smoking in young men. METHODS: A total of 5,590 young Swiss men (mean age = 19.4 years, SD = 1.2) provided data on their substance use, socio-demographic background, religion, health, social context, and personality at baseline and after 18 months. We modelled the predictors of progression to daily cigarette smoking using logistic regression analyses (n = 4,230). RESULTS: In the multivariate overall model, use of cannabis remained among the strongest predictors for the onset of daily cigarette use. Daily cigarette use was also predicted by a lifetime use of at least 50 cigarettes, occasional cigarette use, educational level, religious affiliation, parental situation, peers with psychiatric problems, and sociability. CONCLUSIONS: Our results highlight the relevance of cannabis use compared to other potential predictors of the progression of tobacco use and thereby support the reverse gateway hypothesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".