Cannabis use patterns among people with HIV before and after legalization
Bibliographic record
Abstract
Cannabis use is highly prevalent and detrimental among people with HIV (PWH). Legislative changes in several states altered the legality and accessibility of cannabis. We examined pre-post legislative changes in current, daily, and severe use in PWH in clinical care. PWH engaged in the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort from 3 sites/states were asked about past 3-month cannabis use on a routine clinical assessment of health behavior before and after legalization. A fourth site in a state without legalization served as a comparator. We used linear regression to estimate changes in use prevalence from 1 year before to 1 year after legalization. Among PWH (n=7885), from 1 year before to 1 year after legalization, cannabis use prevalence increased slightly in Boston, MA (32–38 %), Birmingham, AL (26–27 %), and San Diego, CA (25–29 %); and decreased in Seattle, WA (44–41 %). Contemporaneously, daily cannabis use increased modestly (less than 5 %) at all sites. Severe use (cannabis-specific ASSIST score ≥27) decreased or plateaued at all sites. No site showed significant change in prevalence trends of current, daily, or severe use 1 year before and after legalization in linear regression ( p >0.05). Few changes prevailed in cannabis use patterns around dates of legalization among PWH in care in the U.S. Relaxation of cannabis policy does not appear to result in an immediate increase in use among PWH. • Prevalence of current and daily cannabis use did not change among people with HIV following legalization of recreational use. • Severity of use mostly plateaued, but may decrease following legalization. • Relaxation of cannabis policy does not appear to result in increases in use among people with HIV.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".