Factors Associated with Problematic Cannabis Use in a Sample of Medical Cannabis Dispensary Users
Bibliographic record
Abstract
Background: With the recent legalization of cannabis for medical purposes in many countries, there has been an increased number of individuals using such products. While there is considerable evidence indicating that cannabis may have therapeutic effects for a range of different conditions, concerns remain about the risk of developing cannabis use disorders for those at risk, or patients without appropriate clinical guidance. The aim of the present study was to determine the prevalence of problematic cannabis use in a cohort of cannabis users who consumed the drug for medical purposes and to identify potential risk factors. Methods: One hundred individuals who self-identified as using cannabis to improve their mental health were recruited from a community dispensary. Extensive details were collected about subjects' patterns of cannabis use and reasons for use. All subjects completed a structured clinical interview with the Mini-International Neuropsychiatric Interview, while information about perceived stress, depressive symptoms, and somatic symptoms were recorded with the Perceived Stress Scale-10, Beck Depression Inventory, and the Patient Health Questionnaire-15. Results: Rates of problematic cannabis use were high, with 30% meeting the criteria. Only 10% of subjects reported medical cannabis use was recommended by their doctor. Significant risk factors for problematic use included earlier age of cannabis initiation, as well as self-reported use of cannabis products for depression. Conclusions: The prevalence of problematic cannabis use in the community dispensary was higher than expected. Specific risk factors for problematic cannabis use may represent important areas for future intervention to ensure safer consumption for medical purposes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".