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Record W2293160984 · doi:10.1007/s10461-016-1344-7

Beyond Condoms: Risk Reduction Strategies Among Gay, Bisexual, and Other Men Who Have Sex With Men Receiving Rapid HIV Testing in Montreal, Canada

2016· article· en· W2293160984 on OpenAlexafffundabout
Joanne Otis, Amélie McFadyen, Thomas Haig, Martin Blais, Joseph Cox, Bluma Brenner, Robert Rousseau, Gilbert Émond, Michel Roger, Mark A. Wainberg

Bibliographic record

VenueAIDS and Behavior · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsConcordia UniversityCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier de l’Université de MontréalMcGill UniversityHIV Legal NetworkUniversité du Québec à Montréal
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéMinistère de la Santé et des Services sociauxStyrelsen för Internationellt Utvecklingssamarbete
KeywordsCondomHealth psychologyMen who have sex with menPublic healthDemographyMedicineHuman immunodeficiency virus (HIV)HomosexualityAnal sexGerontologyPsychologyFamily medicineSyphilisSociology

Abstract

fetched live from OpenAlex

Gay, bisexual, and other men who have sex with men (MSM) have adapted their sexual practices over the course of the HIV/AIDS epidemic based on available data and knowledge about HIV. This study sought to identify and compare patterns in condom use among gay, bisexual, and other MSM who were tested for HIV at a community-based testing site in Montreal, Canada. Results showed that while study participants use condoms to a certain extent with HIV-positive partners and partners of unknown HIV status, they also make use of various other strategies such as adjusting to a partner's presumed or known HIV status and viral load, avoiding certain types of partners, taking PEP, and getting tested for HIV. These findings suggest that MSM who use condoms less systematically are not necessarily taking fewer precautions but may instead be combining or replacing condom use with other approaches to risk reduction.

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 score0.178
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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

Citations32
Published2016
Admission routes3
Has abstractyes

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