Characterization of mental health in cannabis dispensary users, using structured clinical interviews and standardized assessment instruments
Bibliographic record
Abstract
BACKGROUND: Cannabis is commonly used for its medical properties. In particular, cannabis is purported to have beneficial effects on a wide range of neuropsychiatric conditions. Studies assessing mental health in cannabis dispensary users typically evaluate symptoms using self-report check lists, which provide limited information about symptom severity, and whether subjects meet criteria for a psychiatric diagnosis. There is, therefore, a need for studies which assess mental health in dispensary users with standardized and well validated scientific instruments, such as those used in clinical drug trials. METHODS: One hundred medical cannabis users were recruited from a community dispensary. All subjects completed a structured clinical interview with the Mini-International Neuropsychiatric Interview (MINI). Subjects also completed the Perceived Stress Scale-10, PROMIS Fatigue Scale, PROMIS Sleep Disturbance Scale, Beck Depression Inventory, the Patient Health Questionnaire-15 and the Brief Pain Inventory. Details about cannabis use were also recorded. RESULTS: Lifetime prevalence of mental illness in this cohort was high, and a large proportion of subjects endorsed psychological symptoms. The proportion of subjects who met criteria for classification of a current psychiatric disorder was low for mood disorders, but high for anxiety disorders and substance abuse/dependence. Cannabis use differed between the main psychiatric conditions. CONCLUSIONS: The present results indicate that rates of mental illness may be high in medical cannabis dispensary users. Use of structured clinical assessments combined with standardized symptom severity questionnaires provide a feasible way to provide a more rigorous and detailed evaluation of conditions and symptoms in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".