Correlates of cannabis use in a sample of mental health treatment-seeking Canadian armed forces members and veterans
Bibliographic record
Abstract
OBJECTIVE: Canadian Armed Forces (CAF) members and Veterans are more likely to experience mental health (MH) conditions, such as posttraumatic stress disorder (PTSD), than the general Canadian population. Previous research suggests that an increasing number of individuals are employing cannabis for MH symptom relief, despite a lack of robust evidence for its effectiveness in treating PTSD. This research aimed to: (1) describe the prevalence of current cannabis use among MH treatment-seeking CAF members and Veterans; and (2) estimate the association between current cannabis use and a number of sociodemographic, military, and MH-related characteristics. METHOD: Using cross-sectional intake data from 415 CAF members and Veterans attending a specialized outpatient MH clinic in Ontario, Canada, between January 2018 and December 2020, we estimated the proportion of CAF members and Veterans who reported current cannabis use for either medical or recreational purposes. We used multivariable logistic regression to estimate adjusted odds ratios for a number of sociodemographic, military, and MH-related variables and current cannabis use. RESULTS: Almost half of the study participants (n = 187; 45.1%) reported current cannabis use. Respondents who reported current cannabis use for medical purposes had a higher median daily dose than those who reported current cannabis use for recreational purposes. The multivariable logistic regression identified younger age, lower income, potentially hazardous alcohol use, and increased bodily pain as statistically significant correlates of current cannabis use among our MH treatment-seeking sample. PTSD severity, depressive severity, sleep quality, and suicide ideation were not statistically associated with current cannabis use. CONCLUSIONS: Almost half of our treatment-seeking sample reported current cannabis use for medical or recreational purposes, emphasizing the importance of screening MH treatment-seeking military members and Veterans for cannabis use prior to commencing treatment. Future research building upon this study could explore the potential impact of cannabis use on MH outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".