The Relationship Between Naturalistic Psychedelic Use and Clinical Care in Canada
Bibliographic record
Abstract
Naturalistic psychedelic use among Canadians is common. However, interactions about psychedelic use between patients and clinicians in Canada remain unclear. Via an anonymous survey, we assessed health outcomes and integration of psychedelic use with health care providers (HCP) among Canadian adults reporting past use of a psychedelic. The survey included 2,384 participants, and most (81.2%) never discussed psychedelic use with their HCP. While 33.7% used psychedelics to self-treat a health condition, only 4.4% used psychedelics with a therapist and 3.6% in a clinical setting. Overall, 44.8% (n = 806) of participants were aware of substance testing services, but only 42.4% ever used them. Multivariate regressions revealed that therapeutic motivation, higher likelihood of seeking therapist guidance, and non-binary gender identification were significantly associated with higher odds of discussing psychedelics with one’s primary HCP. Having used a greater number of psychedelics, lower age, non-female gender, higher education, and a therapeutic motivation were significantly associated with higher odds of awareness of substance testing. We conclude that naturalistic psychedelic use in Canada often includes therapeutic goals but is poorly connected to conventional healthcare, and substance testing is uncommon. Relevant training and education for HCPs is needed, along with more visible options for substance testing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".