MétaCan
Menu
Back to cohort
Record W4389564846 · doi:10.3390/brainsci13121698

Motivations for Cannabis Use in Individuals with Social Anxiety Disorder (SAD)

2023· article· en· W4389564846 on OpenAlexaff
Sonja Elsaid, Ruoyu Wang, Stefan Kloiber, Bernard Le Foll, Ahmed N. Hassan

Bibliographic record

VenueBrain Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of TorontoWaypoint Centre for Mental Health CareCentre for Addiction and Mental Health
Fundersnot available
KeywordsCannabisPsychologySocial anxietyAnxietyPsychiatryClinical psychologyCoping (psychology)Young adultDevelopmental psychology

Abstract

fetched live from OpenAlex

Social anxiety disorder (SAD) is a debilitating psychiatric condition. Consequently, it is common for those affected to resort to cannabis to cope with their symptoms. The primary objective of this study was to understand the differences between motivations for cannabis use in adults with and without SAD. We employed convergent, mixed methods to collect the data. Twenty-six individuals (age: 27.9 ± 7.3 years; 54% female) with and twenty-six (age: 27.4 ± 6.7 years; 50% female) without SAD were administered Marijuana Motives Measure (MMM). Motivations to initiate, continue, and maintain cannabis use were assessed in 12/26 participants in both groups using in-depth interviews. Cannabis weekly consumption was 3.8-fold and frequency 1.3-fold higher in the SAD group. Coping (F = 10.02; p <0.001; η2 = 0.46) and social (F = 2.81; p = 0.036; η2 = 0.19) motivations were also higher in the SAD group, after controlling for age, sex, and current CUD. The need to cope with symptoms of SAD may have been the driving force for repeated cannabis consumption. Psychoeducational programs educating children about the risk of using cannabis to cope with SAD should be implemented in vocational settings early on.

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)0.001
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 score0.051
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
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 head0.086
GPT teacher head0.389
Teacher spread0.303 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBrain SciencesSame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207