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Record W4213229867 · doi:10.1080/02791072.2022.2039815

Contextual Parameters Associated with Positive and Negative Mental Health in Recreational Psychedelic Users

2022· article· en· W4213229867 on OpenAlexaff
Kevin O. St. Arnaud, Donald Sharpe

Bibliographic record

VenueJournal of Psychoactive Drugs · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of ReginaConcordia University of Edmonton
Fundersnot available
KeywordsMental healthRecreationPsychologyExpansiveSet (abstract data type)Recreational useClinical psychologyApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

Growing research exploring the utility of psychedelic substances suggests that they not only hold promise for clinical practice but may enhance mental health through recreational use as well. However, given the importance of set and setting for maximizing benefits and minimizing harms of drug use, it is important to develop a foundational understanding of the contextual factors associated with positive and negative mental health in psychedelic users. Accordingly, data were collected using an internet-based survey of psychedelic drug users (n = 511). Hierarchical regression analyses were used to explore to what degree life-time use, frequency of use, dose size, group use, intentions for use, and post-use integration predict mental health in psychedelic users. In particular, using psychedelics with high frequency and to cope with negative affect were found to predict negative mental health. Conversely, using psychedelics in a group setting, with self-expansive intentions, and integrating post-use were found to predict positive mental health. Findings suggest that recreational psychedelic use may either enhance or diminish mental health depending on the contextual parameters of use. Limitations and areas for further research are discussed.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.335
Teacher spread0.308 · 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 designQualitative
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

Citations30
Published2022
Admission routes1
Has abstractyes

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