Psychedelic substitution: altered substance use patterns following psychedelic use in a global survey
Bibliographic record
Abstract
Introduction: Recent research suggests that psychedelics may have potential for the treatment of various substance use disorders. However, most studies to date have been limited by small sample sizes and neglecting to include non-North American and European populations. Methods: We conducted a global, cross-sectional online survey of adults (n = 5,268, 47.2% women) self-reporting past or current psychedelic use and investigated whether psychedelic use was associated with changes in use of other substances. Results: Nearly three-quarters (70.9%; n = 3,737/5,268) reported ceasing or decreasing use of one or more non-psychedelic substances after naturalistic psychedelic use. Among those with previous use, 60.6% (n = 2,634/4,344) decreased alcohol use, 55.7% (n = 1,223/2,197) decreased antidepressant use, and 54.2% (n = 767/1,415) decreased use of cocaine/crack. Over a quarter of the sample indicated that their decrease in substance use persisted for 26 weeks or more following use of a psychedelic. Factors associated with decreased use included a motivation to either decrease one's substance use or self-treat a medical condition. Importantly, 19.8% of respondents also reported increased or initiated use of one or more other substances after psychedelic use, with illicit opioids (14.7%; n = 86/584) and cannabis (13.3%; n = 540/4,064) having the highest proportions. Factors associated with increased substance use included having a higher income and residing in Canada or the US. Discussion: Although limited by cross-sectional study design, this large observational study will help inform future studies aiming to investigate the relationship between substance use patterns and psychedelic use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".