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Record W4298326316 · doi:10.1521/aeap.2022.34.5.365

Perceived Barriers to and Facilitators of Long-Acting Injectable HIV PrEP Use Among Black, Hispanic/Latino, and White Gay, Bisexual, and Other Men Who Have Sex With Men

2022· article· en· W4298326316 on OpenAlexaff
Nguyen K. Tran, Omar Martínez, Ayden I. Scheim, Neal D. Goldstein, Seth L. Welles

Bibliographic record

VenueAIDS Education and Prevention · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsPre-exposure prophylaxisMedicineMen who have sex with menHuman immunodeficiency virus (HIV)Focus groupFamily medicineGerontology

Abstract

fetched live from OpenAlex

Long-acting injectable pre-exposure prophylaxis (LAI-PrEP) was recently approved for HIV prevention as an alternative to daily oral PrEP. We explored preferences and attitudes toward LAI-PrEP among Black, Hispanic/Latino, and White gay, bisexual, and other men who have sex with men (GBM) using focus groups (n = 13) and in-depth interviews (n = 17). Participants expressed differing levels of interest in LAI-PrEP. While important benefits of LAI-PrEP included convenience, provider-facilitated PrEP discussion, and expansion of PrEP options, participants raised concerns about treatment efficacy and side effects, discomfort with needles/injections, cost, and frequency of clinic visits. Our findings highlight ongoing challenges with accessing HIV-prevention tools and provide guidance for developing strategies to enhance LAI-PrEP uptake among GBM.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.339
Teacher spread0.311 · 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 designQualitative
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

Citations19
Published2022
Admission routes1
Has abstractyes

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