Bibliographic record
Abstract
Introduction: Despite rapidly changing cannabis use regulations in Canada, including health care support for Canadian Armed Forces (CAF) Veterans, the prevalence of and reasons for cannabis use in this population have not been studied. Methods: An online 28-item anonymous survey was used to examine the prevalence of cannabis use among CAF Veterans who reported undergoing treatment of PTSD ( N=120). We aimed to estimate the prevalence of cannabis use among CAF Veterans, as well as to explore the reasons for cannabis use in this population. We also examined the relationships between cannabis use and other aspects of health in CAF Veterans, namely other substance use, PTSD symptom severity, and chronic pain severity. Results: Approximately half of the respondents reported a history of cannabis use. Of these, approximately 35.5% reported beginning cannabis use before a military-related trauma, 23% reported beginning after a traumatic event, and 42% reported beginning after release. Participants stated that they used cannabis primarily for relaxation and emotional calm, and for pain management. Only 10% reported its use specifically for PTSD-related symptoms and anxiety. Chronic cannabis users (i.e., one or more years) endorsed a greater number of cannabis abuse symptoms than acute users (i.e., one time to less than three months). Cannabis users were also more likely to use both prescription and non-prescription drugs. No relationships were found between cannabis use and military-related PTSD symptom severity or pain severity. Discussion: Cannabis use, along with other substance use, is common among CAF Veterans, and the reasons for cannabis use vary greatly. Cannabis use does not appear to have an impact on PTSD- and pain-related symptom expression; however, further study is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 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) | 0.005 | 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".