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Record W3160047514 · doi:10.9778/cmajo.20200198

Use of HIV pre-exposure prophylaxis among urban Canadian gay, bisexual and other men who have sex with men: a cross-sectional analysis of the Engage cohort study

2021· article· en· W3160047514 on OpenAlexafffundvenueabout
Joseph Cox, Herak Apelian, Erica E. M. Moodie, Marc Messier-Peet, Trevor Hart, Daniel Grace, David Moore, Nathan J. Lachowsky, Heather L. Armstrong, Jody Jollimore, Shayna Skakoon‐Sparling, Ricky Rodrigues, Darrell H. S. Tan, Mathieu Maheu‐Giroux, Syed W. Noor, Bertrand Lebouché, Cécile Tremblay, Gbolahan Olarewaju, Gilles Lambert

Bibliographic record

VenueCMAJ Open · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto Metropolitan UniversityAIDS VancouverCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCommunity Based Research CentreInstitut National de Santé Publique du QuébecMcGill University Health CentreMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversity of VictoriaUniversity of TorontoPublic Health OntarioUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicinePre-exposure prophylaxisCohortDemographyMen who have sex with menHuman immunodeficiency virus (HIV)Cross-sectional studyLogistic regressionGerontologyRespondentFamily medicineInternal medicineSyphilis

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada, gay, bisexual and other men who have sex with men (GBM) are disproportionately affected by HIV. Our objective was to describe access to HIV pre-exposure prophylaxis (PrEP) and identify factors associated with not using PrEP among self-reported HIV-negative or HIV-unknown GBM. METHODS: This was a cross-sectional analysis of the Engage study cohort. Between 2017 and 2019, sexually active GBM aged 16 years or more in Montréal, Toronto and Vancouver were recruited via respondent-driven sampling (RDS). Participation included testing for HIV and sexually transmitted and blood-borne infections, and completion of a questionnaire. We examined PrEP access using a health care services model and fit RDS-adjusted logistic regressions to determine correlates of not using PrEP among those for whom PrEP was clinically recommended and who were aware of the intervention. RESULTS: A total of 2449 GBM were recruited, of whom 2008 were HIV-negative or HIV-unknown; 1159 (511 in Montréal, 247 in Toronto and 401 in Vancouver) met clinical recommendations for PrEP. Of the 1159, 1100 were aware of PrEP (RDS-adjusted proportion: Montréal 84.6%, Toronto 94.2%, Vancouver 92.7%), 678 had felt the need for PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 39.2%, Toronto 56.1%, Vancouver 49.0%), 406 had tried to access PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 20.6%, Toronto 33.2%, Vancouver 29.6%) and 319 had used PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 14.5%, Toronto 21.6%, Vancouver 21.8%). Not using PrEP was associated with several factors, including not feeling at high enough risk, viewing PrEP as not completely effective, not having a primary care provider and lacking medication insurance. INTERPRETATION: Although half of GBM met clinical recommendations for PrEP, less than a quarter of them reported use. Despite high levels of awareness, a programmatic response that addresses PrEP-related perceptions and health care system barriers is needed to scale up PrEP access among GBM in Canada.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.027
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.359
Teacher spread0.306 · 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

Citations81
Published2021
Admission routes4
Has abstractyes

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