Preferences and Attitudes Toward Music in Nonclinical Uses of Psychedelics
Bibliographic record
Abstract
Background: The resurgent interest in psychedelic-assisted therapy for conditions like depression, end-of-life anxiety, and post-traumatic stress disorder is generating highly promising results. Within this therapeutic context, music's role is multifaceted, seen as a critical environmental component and even akin to a hidden therapist. Contemporary clinical protocols often advocate for music that is lyric-free and unfamiliar, yet these guidelines are based on limited empirical evidence. Methods: Our study, drawing on data from over 2,000 participants in the Canadian Psychedelic Survey (CPS), explores the interplay between music and 11 classical and non-classical psychedelic substances in predominantly non-clinical usage scenarios. This cross-sectional survey included 14 questions delving into patterns of psychedelic use and music preferences in naturalistic environments. Results: Our findings reveal that a substantial majority of respondents experienced both therapeutic benefits and enjoyment from integrating music with psychedelic substances, although the extent of these benefits varied significantly across different substances. Discussion: Contrary to clinical recommendations, only 10% and 22%, respectively, of survey respondents agreed that music for psychedelic experiences should be unfamiliar and without understandable lyrics. Furthermore, our data suggest potential unique benefits of psychedelic experiences devoid of music, particularly with the shortest and longest acting substances. These insights indicate that the prevailing guidelines for music selection in psychedelic therapies may benefit from further, nuanced research into substance-specific effect.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".