The Behavioral Sequelae of Cannabis Use in Healthy People: A Systematic Review
Bibliographic record
Abstract
Background: Cannabis is known to have a broad range of effects on behavior, including experiencing a “high” and tranquility/relaxation. However, there are several adverse behavioral sequalae that can arise from cannabis use, depending on frequency of use, potency (e.g., THC content), age of onset, and cumulative exposure. This systematic review examined evidence for cannabis-related adverse behavioral sequalae in otherwise healthy human subjects. Methods: Following PRISMA guidelines, we conducted a systematic review of cross-sectional and longitudinal studies from 1990 to 2020 that identified cannabis-related adverse behavioral outcomes in subjects without psychiatric and medical co-morbidities from PubMed and PsychInfo searches. Key search terms included “cannabis” OR “tetrahydrocannabinol” OR “cannabidiol” OR “marijuana” AND “anxiety” OR “depression” OR “psychosis” OR “schizophrenia” “OR “IQ” OR “memory” OR “attention” OR “impulsivity” OR “cognition” OR “education” OR “occupation”. Results: Our search detected a total of 2,870 studies, from which we extracted 124 relevant studies from the literature on cannabis effects in the non-clinical population. Effects of cannabis on several behavioral sequelae including cognition, motivation, impulsivity, mood, anxiety, psychosis intelligence, and psychosocial functioning were identified. The preponderance of the evidence suggests that frequency of cannabis use, THC (but not CBD) content, age of onset, and cumulative cannabis exposure can all contribute to these adverse outcomes in individuals without a pre-existing medical condition or psychiatric disorder. The strongest evidence for the negative effects of cannabis are for psychosis and psychosocial functioning. Conclusions: Although more research is needed to determine risk factors for development of adverse behavioral sequelae of cannabis use, these findings underline the importance of understanding vulnerability to the adverse effects of cannabis, which has implications for prevention and treatment of problematic cannabis use.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".