Variations of cannabis-related adverse mental health and addiction outcomes across adolescence and adulthood: A scoping review
Bibliographic record
Abstract
Introduction: Evidence supporting associations between cannabis use and many health outcomes is growing, however it remains unclear how such associations vary across the lifespan. We therefore aim to answer the following questions: (1) Are the risks of cannabis's adverse effects on mental health and addiction-related outcomes different in adolescents than in adults? (2) What are the relationships between these cannabis's adverse effects and (a) an individual's age at first cannabis use, (b) age at assessment, and (c) duration of cannabis use? Methods: We searched Medline, Embase, CINAHL, and PsychINFO from inception to 18 October 2021. Two reviewers independently screened studies and descriptively synthesized results. Results: We included 140 studies. Cannabis effects on mental health and addiction-related outcomes were worse in adolescents, early cannabis initiators and cannabis users who consumed for longest periods. Evidence of worse long-term adverse effects in adolescents was substantial for psychosis, cannabis, and nicotine use disorders; mixed for depression, suicidality, other substance use and disorders; and limited for anxiety. Additionally, acute cannabis exposure had the opposite trend with adults more often reporting adverse effects than adolescents. Conclusion: The available evidence suggests that cannabis use should be delayed as late as possible in adulthood and shortened in duration across the lifespan to decrease the risk of negative outcomes, while emphasizing the need for adapted harm reduction approaches. This scoping review provides evidence on the role of age and duration of exposure as determinants of cannabis-related adverse effects, which may inform prevention and harm reduction strategies. Systematic review registration: https://doi.org/10.17605/OSF.IO/BYG72.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| 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".