Views on and experiences with medicinal cannabis among Canadian veterans who live with pain: A qualitative study
Bibliographic record
Abstract
Background: During fiscal year 2021-2022, Veterans Affairs Canada (VAC) reimbursed 18,388 veterans for medicinal cannabis at a cost of $153 million. Yet, it is not known whether the reimbursement program is producing a net benefit for veterans. Aims: This study investigated the views and experiences Canadian that veterans who live with pain have about medicinal cannabis use, including its use for the management of chronic pain, poor sleep, and emotional distress. Methods: Twelve Canadian veterans who live with pain-eight men, four women; split across four focus groups-were recruited to participate in a semistructured discussion around their experiences with medicinal cannabis use. Results: Using inductive thematic analysis, seven broad categories were identified: (1) cannabis use behaviors, (2) reasons for cannabis use, (3) outcomes from cannabis use, (4) facilitators of cannabis use, (5) barriers to cannabis use, (6) stigma around cannabis use, and (7) questions and concerns about cannabis use. Conclusions: Most veterans initiated cannabis use to manage the symptoms of preexisting medical and/or mental health conditions. Despite some negative side effects, most veterans reported improvements in their overall quality of life, sleep, relationships, mood, and pain. Concern remains around the discrepancy between veterans' qualitative reports of beneficial outcomes from medicinal cannabis use and equivocal findings around the benefit-to-harm ratio in the wider literature. Currently, the VAC reimbursement program remains challenged by unclear indication for which veterans, with what condition(s), at what dose, and in what form medical cannabis is most beneficial.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".