The Potential Effectiveness of Medical Cannabis in the Treatment of Wellness Outcomes for Veterans with Depression
Bibliographic record
Abstract
Depression is a pervasive mental illness with a prevalence of about nine percent of the Canadian population (Van Ameringen et al., 2008). Depression commonly presents with several chronic symptoms that can severely impact the quality of life and mental well-being, such as intrusive thoughts, flashbacks, irritability, anxiety, and sleep disturbances (Sharpless & Barber, 2011). Military veterans are notably present with much higher rates of depression than civilians (Veterans Affairs Canada, 2019). There is no single effective treatment, and persons with depression might receive multiple modes of treatment in combination. To date, few studies have characterized the demographic characteristics of medical marijuana patients or assessed them for pre-post changes in well-being. A mixed methods study was conducted to better understand therapeutic benefits for patients. Here, we aimed to describe physician authorization patterns of medical cannabis products and observe the self-reported effectiveness and wellness outcomes (depression and anxiety) of medical cannabis among Veterans from Avail Cannabis Clinic. Methods: A total of 34 patients were recruited for retrospective chart review. Researchers compared outcomes using the Patient-Health Questionnaire (PHQ-9) for the depression wellness scale. On intake and assessment, doctors interviewed patients on their lived experiences, and the questions were similar to the survey questions, with more opportunities for open-ended responses. Patients were asked to describe preconditions and treatment goals. Results: The average score before the administration of the treatment was 14.6, which according to the scoring guide, denotes moderate to moderately severe depression symptoms. After the treatment was administered, the average PHQ-9 total score decreased to 10.5, which according to the scoring guide highlights mild to moderate depression symptoms. A linear regression was also conducted to determine whether the dosage is a good predictor of the change in depression. The r2 value shows that the model explains roughly 23% (r2 -0.236) of the data. When asked to provide a percentage to characterize the change in symptoms, an average decline of 42% was verbally indicated by participants when asked to report the percentage of decreased pain. A similar self-reported improvement was seen with increased activity, where 38% of participants increased their activity. Conclusion: The interview and survey results indicate that patients seek medical cannabis to relieve medical ailments and have reported better-improved quality of life, mobility, and decreased pain. This finding aligned with previous studies which also found increased overall condition and energy as the most significant perceived benefits. The results may provide further clinical evidence to support the use of medical cannabis for depression symptoms and support more extensive research studies in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".