Cannabis and Mental Health: Adverse Outcomes and Self-Reported Impact of Cannabis Use by Mental Health Status
Bibliographic record
Abstract
Background: Cannabis can induce negative outcomes among consumers with mental health conditions. This study examined medical help-seeking behavior, patterns of adverse effects, and perceived impacts of cannabis among consumers with and without mental health conditions. Methods: Data came from the International Cannabis Policy Study, via online surveys conducted in 2018. Respondents included 6,413 past 12-month cannabis consumers aged 16–65, recruited from commercial panels in Canada and the US. Regression models examined differences in adverse health effects and perceived impact of cannabis among those with and without self-reported past 12-month experience of anxiety, depression, PTSD, bipolar disorder, psychosis. Results: Overall, 7% of past 12-month consumers reported seeking medical help for adverse effects of cannabis, including panic, dizziness, nausea. Help-seeking was greater for those with psychosis (13.8%: AOR = 1.78; 1.11–2.87), depression (8.9%: AOR = 1.57; 1.28–1.93), and bipolar disorder (10.1%: AOR = 1.53; 1.44–2.74). Additionally, 54.1% reported using cannabis to manage symptoms of mental health, with higher rates among those with bipolar (90.8%) and PTSD (90.7%). Consumers reporting >1 condition were more likely to perceive positive impacts on friendships, physical/mental health, family life, work, studies, quality of life (all p < .001). Consumers with psychosis were most likely to perceive negative effects across categories. Conclusion: For conditions with substantial evidence suggesting cannabis is harmful, greater help-seeking behaviors and self-perceived negative effects were observed. Consumers with mental health conditions generally perceive cannabis to have a positive impact on their lives. The relationship between cannabis and mental health is disorder specific and may include a combination of perceived benefits and harms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".