Psilocybin Dispensaries and Online Health Claims in Canada
Bibliographic record
Abstract
Importance: There is growing societal interest in and use of psilocybin. While psilocybin in Canada is illegal outside of clinical trials, there have been anecdotal reports of increasing access via unregulated online purchases and retail dispensaries. Objective: To describe access to and the characteristics of psilocybin dispensaries across Canada and the health claims and warnings made on dispensary websites. Design, Setting, and Participants: This cross-sectional study used systematic web searches and media reports to identify psilocybin dispensaries operating in Canada in May 2024. Data analysis was performed from June 17 to August 29, 2024. Main Outcomes and Measures: Descriptive and geospatial analyses were used to identify the psilocybin dispensary characteristics, product types, and store distribution. Content analysis assessed the nature and frequency of health claims and warnings on websites. Results: As of May 2024, 57 psilocybin dispensaries were identified in Canada (0.18 dispensaries per 100 000 individuals aged ≥15 years) in 15 of Canada's 42 major urban cities (35.7%). Approximately 815 628 (2.6%) of Canadians lived within 1 km of a dispensary. Only 4 of 13 provinces and territories had a dispensary, with most in Ontario and British Columbia. Of the 57 stores, 35 (61.4%) were part of a chain (≥2 stores owned by a single company) and 52 (91.2%) had an online presence. Stores sold a wide variety of products, including dried mushrooms (100.0%), microdosing capsules (97.8%), psilocybin-infused chocolate (91.3%) and gummies (93.4%), and most stores (65.2%) sold products mimicking popular food brands. Among stores with websites, 86.4% claimed mental health benefits of psilocybin (eg, alleviating anxiety). While 86.4% of websites provided health warnings, relevant warnings, such as those about use while driving (9.1%), during pregnancy (13.6%), or in individuals with a history of psychosis, schizophrenia, or bipolar disorder (31.8%) were rare. Conclusions and Relevance: In this study, psilocybin retailers, who were present in over a third of major Canadian cities, made a variety of unverified health claims and lacked warnings of potential harms, suggesting the need for greater regulatory measures to protect the public.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".