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Record W3205556574 · doi:10.1080/15299716.2021.1982102

Longitudinal Analysis of HIV Risk and Substance Use Patterns for Men Who Have Sex With Men and Women and Men Who Have Sex With Men Only

2021· article· en· W3205556574 on OpenAlexafffund
Eric Abella Roth, Zishan Cui, Heather L. Armstrong, Ashleigh J. Rich, Nathan J. Lachowsky, Paul Sereda, Kiffer G. Card, Nic Bacani, David Moore, Robert S. Hogg

Bibliographic record

VenueJournal of Bisexuality · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British ColumbiaAIDS VancouverSimon Fraser UniversityUniversity of Victoria
FundersNational Institute on Drug AbuseCanadian Institutes of Health Research
KeywordsMen who have sex with menMultivariate analysisDemographyPopulationLongitudinal studyMedicineSexual orientationClinical psychologyPsychologyHuman immunodeficiency virus (HIV)Internal medicineSocial psychologyImmunologySyphilis

Abstract

fetched live from OpenAlex

Men who have sex with men and women (MSMW) experience discrimination from same-sex and heterosexual communities partially because of perceptions that they engage in high-risk sexual behavior, have elevated polysubstance use levels, and constitute an HIV bridge population. We used a longitudinal multivariate generalized linear mixed model comparing sexual risk and substance use patterns for men who have sex with men only (MSMO) with MSMW in the same cohort study. Data consisted of 771 men reporting 3,705 sexual partnerships from 2012 to 2017. For high-risk sexual behavior, multivariate results showed nonsignificant (p > .05) differences for partner number and commercial sex work and significantly less (p < .05) HIV prevalence and condomless anal sex. However, MSMW had significantly higher levels of hallucinogen and prescription opioid use as well as substance treatment histories. Only one HIV-positive MSMW had a transmittable viral load, negating the concept of MSMW being an HIV bridge population. Results indicate the need for additional longitudinal studies comparing MSMO and MSMW.

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 score0.043
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.341
Teacher spread0.302 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

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