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Record W2249068781 · doi:10.1177/2158244014529776

HIV Testing by Black MSM in Toronto

2014· article· en· W2249068781 on OpenAlexaffabout
Clemon George, Lydia Makoroka, Sean B. Rourke, Barry D. Adam, Robert S. Remis, Winston Husbands, Stanley Read

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of WindsorUniversity of TorontoAIDS Committee of TorontoOntario Tech University
Fundersnot available
KeywordsMen who have sex with menMedicineDemographyPublic healthSeroprevalencePsychological interventionLogistic regressionOddsHomosexualityFamily medicineHuman immunodeficiency virus (HIV)GerontologyEnvironmental healthPsychologyImmunologyPsychiatrySyphilisSociology

Abstract

fetched live from OpenAlex

Surveillance data suggest that Black men who have sex with men (MSM) in Canada contribute to a higher than expected percentage of new HIV diagnoses. HIV testing is an important part of the HIV reduction strategy in Canada and the Public Health Agency of Canada recommends HIV testing as a component of periodic routine medical care. A cross-sectional survey was conducted among Black MSM in Toronto to determine the factors associated with HIV testing. One hundred sixty-five men were recruited and completed a self-administrative questionnaire. The majority of men identified as gay/homosexual. Lifetime history of HIV testing was reported at 85%, of whom 50% had tested within the last 6 months. Self-reported HIV seroprevalence was 24%. In logistic regression, variables associated with ever testing for HIV were “having friends or family with HIV” and “regularly attending religious services.” Although HIV testing appears to be common among Black MSM in Canada, young Canadian-born men were less likely to test. This observation highlights the need to examine place of birth when tailoring health interventions for Black MSM.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.001

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 head0.036
GPT teacher head0.374
Teacher spread0.338 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2014
Admission routes2
Has abstractyes

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