Perceptions and Use of Alcohol and Medical Cannabis among Canadian Military Veterans Living with PTSD
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) is a common psychiatric diagnosis among Canadian military veterans, and alcohol and medical cannabis are commonly used by this group to cope with PTSD symptoms. This paper is part of a larger study that examined a cohort of 5 veterans, over a 1-year period, who used both medical cannabis and alcohol and were matched with a PTSD service dog. This paper compares the perceptions and use of alcohol and medical cannabis among the veterans to cope with their PTSD symptoms and outlines key implications. Semi-structured interviews offer insight into similarities and differences between the veterans’ perceptions and use of the two substances. Both substances are used by the veterans to manage their PTSD symptoms, typically worsening them if used in excess. Medical cannabis is a prescribed medication; however, it is perceived by the veterans to be associated with a negative discourse and in particular stigma. This is not the case for alcohol. The veterans identified alcohol use as more influenced by social norms and perceived it as more of a concern for addiction compared to medical cannabis. This did not, however, appear to impact the level of alcohol use. These findings offer unique insight into the military culture’s general acceptance of alcohol but not medical cannabis use. This has possible implications for veterans’ use of alcohol and/or medical cannabis to help manage their PTSD symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".