Mental Health and Dispositional Predictors of Simultaneous versus Concurrent Cannabis and Alcohol Use in a Canadian Context
Bibliographic record
Abstract
Objective: Cannabis has become more available in Canada since its legalization in 2018. Many individuals who use cannabis also use alcohol (co-use), which can be used either at the same time such that their effects overlap (simultaneous use) or at different times (concurrent use). Though studies have identified predictors of co-use relative to single-substance use, less is known about the predictors of specific types of co-use. The present study examined the mental health and dispositional predictors of simultaneous relative to concurrent use of the two legal substances (cannabis and alcohol) among adults in Canada. Method: = 1,761) were recruited from Academic Prolific and six Canadian universities. Participants completed online self-report measures of demographic characteristics, cannabis and alcohol co-use, mental health symptoms, impulsivity, and personality traits. Results: Binary logistic regression analyses revealed that when independent variables were each examined individually, greater severity of depression, anxiety, PTSD, and ADHD symptoms; greater negative urgency and lack of premeditation; and greater impulsivity each predicted an increased likelihood of reporting past-year simultaneous use relative to concurrent use. When independent variables were grouped into three separate models (mental health, impulsivity, and personality variables), greater anxiety symptom severity, ADHD symptom severity, negative urgency, and sensation seeking were each uniquely associated with an increased likelihood of simultaneous relative to concurrent use. Conclusions: Individuals with elevated anxiety and ADHD symptoms, as well as negative urgency and sensation seeking, may be more inclined to engage in simultaneous use to self-medicate and achieve greater symptom reduction. Future studies may examine the directionality of these relations and motives (e.g., coping) that may differentiate simultaneous and concurrent use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".