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Record W2341591042 · doi:10.1080/19317611.2016.1168903

Nonmonosexual Partnerships: Information, Motivation and Self-Efficacy Among Methamphetamine-Using Men Who Have Sex with Men Who Also Have Sex with Women or Transgender Persons

2016· article· en· W2341591042 on OpenAlexaff
Syed W. Noor, J. Michael Wilkerson, Vanessa Schick, Alex Iantaffi

Bibliographic record

VenueInternational Journal of Sexual Health · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Mental Health
KeywordsMen who have sex with menCondomTransgenderPsychologyHuman immunodeficiency virus (HIV)Anal sexClinical psychologySex partnersMedicineDemographyDevelopmental psychologySyphilisFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Sex with more than one gender is associated with higher substance use, and sexual HIV risk. METHODS: We examined knowledge, motivation, and self-efficacy to engage in safer substance use and sexual behavior among methamphetamine-using U.S. men who have sex with more than one gender (N=343). RESULTS: Almost half(46.2%) of the men reported having sex with a man and a woman or transgender partner in the last 30 days. Compared to monosexual MSM, non-monosexual MSM reported greater condom use self-efficacy however, they reported more sexual partners who inject drugs. CONCLUSION: We observed distinct differences between men who do or do not have sex with more than one gender.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.371
Teacher spread0.304 · 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 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
Published2016
Admission routes1
Has abstractyes

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