Patterns and Correlates of Cannabis Use among Individuals with HIV/AIDS in Maritime Canada
Bibliographic record
Abstract
BACKGROUND: The prevalence of cannabis use in HIV-infected individuals is high and its long-term effects are unclear. METHODS: The prevalence, perceived benefits and consequences, and predictors of cannabis use were studied using a cross-sectional survey in two immunodeficiency clinics in Maritime Canada. RESULTS: Current cannabis use was identified in 38.5% (87 of 226) of participants. Almost all cannabis users (85 of 87 [97.7%]) acknowledged its use for recreational purposes, with 21.8% (19 of 87) reporting medicinal cannabis use. The majority of patients enrolled in the present study reported mild or no symptoms related to HIV (n=179). Overall, 80.5% (70 of 87) of the cannabis-using participants reported a symptom-relieving benefit, mostly for relief of stress, anorexia or pain. Participants consumed a mean (± SD) of 18.3±21.1 g of cannabis per month and spent an average of $105.15±109.87 on cannabis per month. Cannabis use was associated with rural residence, lower income level, driving under the influence of a substance, and consumption of ecstasy and tobacco. Income level, ecstasy use and tobacco use were retained as significant predictors in regression modelling. Cannabis use was not associated with adverse psychological outcomes. DISCUSSION: Prolonged previous cannabis consumption and the substantial overlap between recreational and medicinal cannabis use highlight the challenges in obtaining a tenable definition of medicinal cannabis therapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".