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Record W2039552055 · doi:10.1258/ijsa.2011.011057

Awareness of, usage of and willingness to use HIV pre-exposure prophylaxis among men in downtown Toronto, Canada

2011· article· en· W2039552055 on OpenAlexafffundabout
Mathew Leonardi, E Lee, Darrell H. S. Tan

Bibliographic record

VenueInternational Journal of STD & AIDS · 2011
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Michael's HospitalHassle Free ClinicUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicinePre-exposure prophylaxisMen who have sex with menDowntownDemographicsLogistic regressionHuman immunodeficiency virus (HIV)Family medicineSexual health clinicReproductive healthEnvironmental healthDemographyPopulationInternal medicine

Abstract

fetched live from OpenAlex

Pre-exposure prophylaxis (PrEP) is a promising strategy whereby HIV-uninfected people could take antiretroviral (ARV) medications to reduce their risk of HIV acquisition. Reports suggest that unsupervised PrEP use has been occurring in gay communities of USA cities before human safety and efficacy data became available. We administered a 20-item questionnaire to men undergoing HIV testing at Hassle Free Clinic, a sexual health clinic in the gay village of Toronto. Questionnaire items enquired about demographics, sexual partners, substance use and awareness of, usage of and willingness to use PrEP. Logistic regression was used to identify characteristics associated with PrEP-related outcomes. Of 256 participants, 11.7% were aware of PrEP, with more men who have sex with men (MSM) aware (14.1%) than non-MSM (4.9%). No participants reported PrEP usage. Willingness to consider PrEP use was high and associated with high-risk activities, suggesting opportunities for PrEP use in the future.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.307
Teacher spread0.284 · 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

Citations58
Published2011
Admission routes3
Has abstractyes

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