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Record W4405128970 · doi:10.1080/09540121.2024.2437564

Use of HIV pre-exposure prophylaxis among men who have sex with men: low uptake and retention despite high-risk indications

2024· article· en· W4405128970 on OpenAlexafffund
Lauren Orser, Paul MacPherson, Patrick O’Byrne

Bibliographic record

VenueAIDS Care · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersOntario HIV Treatment Network
KeywordsPre-exposure prophylaxisMedicineHuman immunodeficiency virus (HIV)Men who have sex with menReferralStigma (botany)Family medicinePsychiatry

Abstract

fetched live from OpenAlex

HIV PrEP is over 99% effective in preventing HIV when medication adherence is high. Despite this, uptake and retention in PrEP care remains less than optimal. We investigated whether gbMSM with objective risk factors for HIV who were automatically offered PrEP would have higher uptake and retention in PrEP care. For this, gbMSM with clinical evidence of HIV risk received a reflexive offer for PrEP from a nurse. The number of offers, referral acceptance, presentation to the first appointment, initiation and retention at 6 months were examined. Of 1181 gbMSM with objective HIV risk factors who were offered PrEP, only 50% accepted, 28% initiated and 16% remained on PrEP at 6 months. Loss across the cascade was more pronounced for youth. We found a notable disconnect between recent STI diagnosis and acceptance, initiation and retention in PrEP. This notwithstanding, 137 at-risk individuals were retained on PrEP because of an active offer. PrEP delivered by nurses was as effective as that delivered by infectious disease physicians. While active offer PrEP successfully brought at-risk individuals into care, more work is required to understand the perceptions of risk, the benefits and challenges of PrEP use, and how stigma and structural barriers affect retention among diverse groups affected by HIV.

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 score0.192
Threshold uncertainty score0.424

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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