Cannabis Consumption in People Living with HIV: Reasons for Use, Secondary Effects, and Opportunities for Health Education
Bibliographic record
Abstract
Abstract Introduction: Rates of cannabis consumption range from 40% to 74% among people living with HIV (PLWH). Little is known about the reasons for cannabis use, related modes of administration, effectiveness for symptom relief, or undesirable effects in the modern antiretroviral therapy (ART) era. Our aim was to conduct an exploratory study to identify potential areas for further evaluation and intervention. Materials and Methods: From January to June 2018, health care providers at the Chronic Viral Illness Service in Montreal, Canada, asked their patients about cannabis use during routine visits. Patients reporting cannabis use were invited to complete a 20-min coordinator-administered questionnaire. Questions related to patterns of use, modes of administration, reasons for use, secondary effects, and HIV health-related factors (e.g., adherence to ART). Results: One hundred and four PLWH reporting cannabis use participated. Median age was 54 years (interquartile range [IQR] 46–59), 13% were female, and 42% were HIV-Hepatitis C co-infected. Median CD4 count was 590 cells/mm 3 (IQR 390–821), 95% of participants were on ART, and 88% had suppressed viral loads. Reported cannabis use was more than once daily (32%); daily (25%); weekly (22%); monthly (17%); and rarely (twice to thrice per year; 6%). The majority of participants (97%) smoked dry plant cannabis. Other modes included vaping (12%), capsules (2%), edibles (21%), and oils (12%). Common reasons for cannabis use were for pleasure (68%) and to reduce anxiety (57%), stress (55%), and pain (57%). Many participants found cannabis “quite effective” or “extremely effective” (45%) for symptom relief. Secondary effects included feeling high (74%), increased cough (45%), paranoia (22%), palpitations (20%), and increased anxiety (21%). Over two-thirds of participants indicated that secondary effects were not bothersome at all. Most participants (68%) rarely missed doses of their ART, while 27% missed occasionally (once to twice per month). The most commonly accessed sources of information about cannabis were friends (77%) and the internet (55%). Conclusion: The most common reasons for cannabis use in our population were for pleasure, followed by reduction of stress/anxiety and symptoms associated with a medical condition. Most smoke cannabis and rate cannabis as quite effective for symptom relief. While many participants experience secondary effects, most are not bothered by these symptoms. Amid widespread changes in the regulatory landscape of recreational cannabis, health care providers should be prepared to answer questions about cannabis.
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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 | 0.003 | 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 | 0.001 | 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".