Cannabis consumption patterns, adverse events, and cannabis risk beliefs: A latent profile analysis in WA State
Bibliographic record
Abstract
Cannabis legalization has increased the diversity of products available to people wishing to purchase cannabis. Understanding profiles of people who use cannabis, including use of different product types and how these relate to adverse events and risk beliefs may aid public health professionals, clinicians, and people who use cannabis who are seeking to reduce the risk of cannabis use. This cross-sectional study used data from Washington State residents between 16 and 65 years old collected between 2019 and 2022 as part of the International Cannabis Policy Study to characterize of patterns of use through Latent Profile Analysis. The study describes six cluster groups made up of those who reported past year cannabis use (N = 3298) that differed by frequency of use of cannabis product types, ranging from the lowest use group that averaged weekly use of primarily flower to a group characterized by daily use of concentrates. Contrasting with clinical studies that indicate that adverse events increase with THC levels and frequency of use, this group reported significantly fewer adverse events than the group with the next most frequent use who reported a greater variety of product types. These findings may be influenced by transitions between groups, which are not captured in this cross-sectional study. The four groups with most frequent use and greatest variety of product types, were all significantly more likely to self-identify as "addicted" than the lowest use, primarily flower, group. There were few differences in risk beliefs between groups. Efforts to reduce cannabis risk should focus on reducing frequency of use and possibly limiting polymodal cannabis use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".