Cannabis use and psychotherapeutic outcomes for PTSD in Canadian military and veterans
Bibliographic record
Abstract
Canadian Armed Forces (CAF) members and veterans are increasingly choosing cannabis to manage mental health symptoms, including those of Post-Traumatic Stress Disorder (PTSD). Although psychotherapeutic treatments are recommended for PTSD, there is a lack of high-quality research regarding the potential impacts of cannabis use on treatment outcomes. The little research that has examined cannabis use and psychotherapeutic treatment for PTSD has produced mixed results. There is also a lack of detailed information on cannabis use characteristics (e.g., frequency, THC potency, CBD:THC ratio) of CAF members and veterans and how these may relate to treatment outcomes. Further, little is known about CAF members’ and veterans’ experiences of cannabis use during psychotherapeutic treatment. This study utilized a pre-post-follow-up design to examine psychotherapeutic treatment outcomes of 11 treatment-seeking CAF members and veterans with clinical or sub-clinical PTSD. Multilevel modelling was used to compare outcomes of those who use cannabis (n = 4), and those who did not use cannabis (n = 7). No statistically significant results were found, however the comparison was of low power due to sample size. A regression analysis examined the potential relationships between cannabis-use characteristics and PTSD symptoms at pre-treatment (which included all participants who for whom there was adequate data), however, again, there were no statistically significant results and the analysis was of low power (n = 9). Descriptive results on cannabis use characteristics highlighted a high degree of heterogeneity even in the small sample suggesting that future research that measures cannabis in a more detailed way may aid in clarifying the currently mixed findings. Thematic analysis of open-ended questions highlighted several important themes regarding factors influencing cannabis use decisions and experiences. Careful consideration of both benefits and drawbacks of cannabis, as well as personal beliefs and history, appear to play important roles in cannabis use choices and experiences. Clinicians could benefit from the knowledge that cannabis use varies considerably in this population, and from understanding factors that may relate to their clients’ cannabis use decisions and experiences. Clinicians could use themes identified in this study as starting points for more informed conversations around cannabis use with clients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".