Understanding Motivations, Perceptions, And Effects of Cannabis Use in Individuals with Mood and Anxiety Disorders
Bibliographic record
Abstract
In individuals with mood and anxiety disorders, cannabis use is common, yet its benefits and risks remain uncertain amid Canada's cannabis legalization and availability of various products. This study comprehensively reviews literature on perceptions, motivations, and effects of cannabis use among people with mood and anxiety disorders, alongside a clinical investigation comprising an anonymous survey and a qualitative interview with the same population. Two hundred and nine participants (130 current users, 41 past users, 38 non-users) completed the anonymous survey and 36 adult participants with mood and anxiety disorders including OCD or PTSD who currently use cannabis completed the qualitative virtual interviews. Survey data was analysed using SPSS and the interview data was analysed using thematic analysis in NVivo. Overall, more negative effects including cognitive dysfunction, worsening of mood and anxiety symptoms were acknowledged with ongoing cannabis use. Reasons for use include coping with mental health symptoms, sleep problems and managing medication side effects. Concerns arise from early initiation of cannabis before age 18, combined medical and recreational usage, and limited medical consultation. Individuals with coping motives and enhancement motives had a greater risk of having cannabis use-related problems as measured by the Cannabis Use Disorder Identification Test (CUDIT-R). Information from this study on motivations, potential risks including maladaptive use and dependence, mixed recreational and medical cannabis use, negative effects on health outcomes, as well as limited medical consultation, can contribute to and inform development of education, prevention, and intervention strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".