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Record W4391823643 · doi:10.32920/25219346.v1

Psychosocial Mechanisms of Methamphetamine Use Among Gay, Bisexual, and Other Men Who Have Sex with Men: An Integrated Theoretical Approach

2024· preprint· en· W4391823643 on OpenAlexaff
Graham W. Berlin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto Metropolitan UniversityUniversity of Victoria
Fundersnot available
KeywordsPsychologyPsychosocialMethamphetamineStructural equation modelingMen who have sex with menDistressClinical psychologyCoping (psychology)Psychological interventionSexual minorityHomosexualityHuman immunodeficiency virus (HIV)Substance abuseDevelopmental psychologyPsychiatrySocial psychologyMedicineSexual orientation

Abstract

fetched live from OpenAlex

I proposed an integrated theoretical model as a framework to examine psychosocial factors associated with gay, bisexual, and other men who have sex with men’s (GBM) methamphetamine use and problematic methamphetamine use. The proposed model was estimated using structural equation modeling among 2449 GBM. The model was good fit for the data among the HIV negative GBM and GBM living with HIV subsamples. Heterosexist discrimination and childhood sexual abuse were associated with psychological distress. In turn, psychological distress was associated methamphetamine use in the past six months indirectly through cognitive escape and sexual compulsivity (HIV negative subsample only). Findings demonstrate the utility of integrating minority stress theory and other models into treatment models for methamphetamine using GBM. More specifically, the results support the need for GBM-specific interventions that address heterosexism and provide adaptive coping strategies to prevent and reduce the harms of methamphetamine use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.373
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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