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Record W4409733390 · doi:10.1177/10778012251334769

Higher Sexual Relationship Power Associated With Optimal HIV Treatment and Care Outcomes Among Women Living With HIV in Heterosexual Relationships in Metro Vancouver

2025· article· en· W4409733390 on OpenAlexafffundabout
He Cao, Kate Shannon, Melissa Braschel, Mika Ohtsuka, Charlie Zhou, Kathleen Deering

Bibliographic record

VenueViolence Against Women · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institutes of HealthCanadian HIV Trials Network, Canadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsLogistic regressionHuman immunodeficiency virus (HIV)MedicineBivariate analysisPsychological interventionDemographyCohortGerontologyPsychologyFamily medicinePsychiatrySociology

Abstract

fetched live from OpenAlex

In this study among a longitudinal cohort of women living with human immunodeficiency virus (HIV) in Metro Vancouver, Canada (2014-2019), we used bivariate and multivariable logistic regression with generalized estimating equations to investigate associations between low, medium, and high relationship power and two outcomes among women in heterosexual relationships: (1) being on antiretroviral therapy (ART); (2) optimal ART use. Multivariable analysis suggested that high and medium relationship power were significantly associated with being on ART and optimal ART use. These findings suggest the critical importance of relationship power screening, strength-focused couples-based interventions and structural approaches to address gendered inequities, norms and HIV stigma.

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)0.000
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 score0.057
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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