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Record W4405735206 · doi:10.1080/02791072.2024.2446445

Preferences, Perceptions, and Environmental Considerations of Natural and Synthetic Psychedelic Substances: Findings from the Global Psychedelic Survey

2024· article· en· W4405735206 on OpenAlexaff
Omer A. Syed, Rotem Petranker, Emily C. Fewster, Valentyn Sobolenko, Zeina Beidas, Muhammad Ishrat Husain, Stephanie Lake, Philippe Lucas

Bibliographic record

VenueJournal of Psychoactive Drugs · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsCentre for Addiction and Mental HealthYork UniversityMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsPsilocybinMescalineNatural (archaeology)PreferencePsychologyPerceptionOverexploitationRock artSocial psychologyHallucinogenGeographyEcologyPsychiatryArchaeology

Abstract

fetched live from OpenAlex

Although several studies have well described the characteristics of people who use psychedelics alongside their motivations and beliefs, little research has examined the preferences surrounding the source of psychedelic substances. In an anonymous online survey, we collected data from 6,379 consumers of 11 different psychedelic substances from 85 different countries, exploring their preferences and perceptions on natural and synthetic psychedelics. There was a strong preference of natural sources over synthetic alternatives for psilocybin (75%), DMT (56%), and mescaline (56%). Moreover, 50.8% of respondents believed that the source impacts the psychedelic's psychological and physiological effects, while 34.4% of respondents had a neutral stance on the topic. Despite the preference for natural sources, 67.7% of respondents agreed to switch to using synthetic alternatives to psychedelic substances if it would lessen the environmental impacts caused by the overharvesting of natural sources. This study presents novel insights into consumer preferences on the source of popular psychedelic substances. This international survey is limited to respondents primarily belonging to anglophone regions of the world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.329
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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