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Record W4200192487 · doi:10.2196/35590

Integrating and Disseminating Pre-Exposure Prophylaxis (PrEP) Screening and Dispensing for Black Men Who Have Sex With Men in Atlanta, Georgia: Protocol for Community Pharmacies

2021· article· en· W4200192487 on OpenAlexvenueno aff
Natalie D. Crawford, Kristin R V Harrington, Daniel I. Alohan, Patrick S. Sullivan, David P. Holland, Donald G. Klepser, Alvan Quamina, Aaron J. Siegler, Henry N. Young

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institute of Mental Health
KeywordsPre-exposure prophylaxisPharmacyMedicineMen who have sex with menFamily medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: Black men who have sex with men (BMSM) suffer from alarmingly high rates of HIV in the United States. Pre-exposure prophylaxis (PrEP) can reduce the risk of HIV infection by 99% among men who have sex with men, yet profound racial disparities in the uptake of PrEP persist. Low PrEP uptake in BMSM is driven by poor access to PrEP, including inconvenient locations of PrEP-prescribing physicians, distrust of physicians, and stigma, which limit communication about PrEP and its side effects. Previous work indicates that offering HIV prevention services in pharmacies located in low-income, underserved neighborhoods is feasible and can reduce stigma because pharmacies offer a host of less stigmatized health services (eg, vaccinations). We present a protocol for a pharmacy PrEP model that seeks to address challenges and barriers to pharmacy-based PrEP specifically for BMSM. OBJECTIVE: We aim to develop a sustainable pharmacy PrEP delivery model for BMSM that can be implemented to increase PrEP access in low-income, underserved neighborhoods. METHODS: This study design is a pilot intervention to test a pharmacy PrEP delivery model among pharmacy staff and BMSM. We will examine the PrEP delivery model's feasibility, acceptability, and safety and gather early evidence of its impact and cost with respect to PrEP uptake. A mixed-methods approach will be performed, including three study phases: (1) a completed formative phase with qualitative interviews from key stakeholders; (2) a completed transitional pilot phase to assess customer eligibility and willingness to receive PrEP in pharmacies during COVID-19; and (3) a planned pilot intervention phase which will test the delivery model in 2 Atlanta pharmacies in low-income, underserved neighborhoods. RESULTS: Data from the formative phase showed strong support of pharmacy-based PrEP delivery among BMSM, pharmacists, and pharmacy staff. Important factors were identified to facilitate the implementation of PrEP screening and dissemination in pharmacies. During the transitional pilot phase, we identified 81 individuals who would have been eligible for the pilot phase. CONCLUSIONS: Pharmacies have proven to be a feasible source for offering PrEP for White men who have sex with men but have failed to reach the most at-risk, vulnerable population (ie, BMSM). Increasing PrEP access and uptake will reduce HIV incidence and racial inequities in HIV. Translational studies are required to build further evidence and scale pharmacy-based PrEP services specifically for populations that are disconnected from HIV prevention resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35590.

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)0.062
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.039
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0070.002
Scholarly communication0.0030.003
Open science0.0050.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0550.012

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 head0.148
GPT teacher head0.534
Teacher spread0.386 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations13
Published2021
Admission routes1
Has abstractyes

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