Nicotine and Alcohol Use as Predictors of Recreational Cannabis Use in Adolescence: A Systematic Review and Narrative Synthesis
Bibliographic record
Abstract
Background: The prevalence of recreational cannabis use among adolescents is a growing public health concern due to its link to short- and long-term adverse effects on adolescents’ wellbeing, physical health, mental health, and interpersonal behaviors. Method: Five databases were searched from inception to March 17, 2023, for exposure (nicotine product, alcohol) and outcome (recreational cannabis) in adolescents (persons aged 10–19 years). The studies were screened independently by two reviewers, and the quality of the studies was assessed with Newcastle Ottawa and AXIS tool. PRISMA guidelines were employed in this review. Result: Twenty-one (21) studies involving 2,778,406 adolescents were included in the appraisal and heterogeneity was found among these studies. Ascertainment bias was commonly detected in thirteen (13) of the included studies. Among the substances examined as potential exposures, nicotine-product use emerged as a significant factor associated with future cannabis use among adolescents, particularly in mid-adolescence and in places where recreational cannabis use has been legalized. Conclusion: Current evidence suggests an association between nicotine-product use and subsequent recreational cannabis use among adolescents. However, further research is needed to establish causality between exposure to nicotine substances and the use of recreational cannabis within this age demographic. Additionally, there is a need for the development of prevention programs and targeted policies that continuously inform and update this vulnerable sub-population about the risks associated with cannabis use for leisure.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".