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Record W4304191898 · doi:10.2196/42585

Nurse Practitioner–Led Integrated Rapid Access to HIV Prevention for People Who Inject Drugs (iRaPID): Protocol for a Pilot Randomized Controlled Trial

2022· article· en· W4304191898 on OpenAlexvenueno aff
Antoine Khati, Frederick L. Altice, David Vlahov, Jessica Lee, Terry Bohonnon, Jeffrey A. Wickersham, Francesca Maviglia, Nicholas Copenhaver, Roman Shrestha

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on Drug Abuse
KeywordsProtocol (science)MedicineHuman immunodeficiency virus (HIV)Randomized controlled trialNursingFamily medicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The ongoing volatile opioid epidemic remains a significant public health concern, alongside continued outbreaks of HIV and hepatitis C virus among people who inject drugs. The limited access to and scale-up of medications for opioid use disorder (MOUD) among people who inject drugs, coupled with multilevel barriers to pre-exposure prophylaxis (PrEP) uptake, makes it imperative to integrate evidence-based risk reduction and HIV prevention strategies in innovative ways. To address this need, we developed an integrated rapid access to HIV prevention program for people who inject drugs (iRaPID) that incorporates same-day PrEP and MOUD for this population. OBJECTIVE: The primary objective of this pilot study is to assess the feasibility and acceptability of the program and evaluate its preliminary efficacy on PrEP and MOUD uptake for a future randomized controlled trial (RCT). We also aim to explore information on the implementation of the program in a real-world setting using a type I hybrid implementation trial design. METHODS: Using a type I hybrid implementation trial design, we are pilot testing the nurse practitioner-led iRaPID program while exploring information on its implementation in a real-world setting. Specifically, we will assess the feasibility and acceptability of the iRaPID program and evaluate its preliminary efficacy on PrEP and MOUD uptake in a pilot RCT. The enrolled 50 people who inject drugs will be randomized (1:1) to either iRaPID or treatment as usual (TAU). Behavioral assessments will occur at baseline, and at 1, 3, and 6 months. Additionally, we will conduct a process evaluation of the delivery and implementation of the iRaPID program to collect information for future implementation. RESULTS: Recruitment began in July 2021 and was completed in August 2022. Data collection is planned through February 2023. The Institutional Review Boards at Yale University and the University of Connecticut approved this study (2000028740). CONCLUSIONS: This prospective pilot study will test a nurse practitioner-led, integrated HIV prevention program that incorporates same-day PrEP and MOUD for people who inject drugs. This low-threshold protocol delivers integrated prevention via one-stop shopping under the direction of nurse practitioners. iRaPID seeks to overcome barriers to delayed PrEP and MOUD initiation, which is crucial for people who inject drugs who have had minimal access to evidence-based prevention. TRIAL REGISTRATION: ClinicalTrials.gov NCT04531670; https://clinicaltrials.gov/ct2/show/NCT04531670. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42585.

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)0.058
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.092
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.047
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0120.007
Bibliometrics0.0030.004
Science and technology studies0.0050.005
Scholarly communication0.0060.006
Open science0.0040.003
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0920.016

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 head0.196
GPT teacher head0.566
Teacher spread0.370 · 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 designRandomized trial
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

Citations5
Published2022
Admission routes1
Has abstractyes

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