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Record W4365444381 · doi:10.2196/42418

A Novel Capacity-Strengthening Intervention for Frontline Harm Reduction Workers to Support Pre-exposure Prophylaxis Awareness-Building and Promotion Among People Who Use Drugs: Formative Research and Intervention Development

2023· article· en· W4365444381 on OpenAlexvenueno aff
Jennifer L. Glick, Leanne Zhang, Joseph G. Rosen, Karla Yaroshevich, Bakari Atiba, Danielle Pelaez, Ju Nyeong Park

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institute of General Medical SciencesCenter for AIDS Research, University of WashingtonNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesJohns Hopkins University
KeywordsHarm reductionFormative assessmentPsychological interventionIntervention (counseling)Promotion (chess)MedicinePre-exposure prophylaxisGeneral partnershipHarmPopulationNursingHealth promotionCapacity buildingPsychologyMedical educationFamily medicineHuman immunodeficiency virus (HIV)Environmental healthPublic healthSocial psychologyPolitical sciencePedagogyMen who have sex with men

Abstract

fetched live from OpenAlex

BACKGROUND: HIV prevalence among people who use drugs (PWUD) in Baltimore, Maryland, is higher than among the general population. Pre-exposure prophylaxis (PrEP) is a widely available medication that prevents HIV transmission, yet its usefulness is low among PWUD in Baltimore City and the United States. Community-level interventions to promote PrEP uptake and adherence among PWUD are limited. OBJECTIVE: We describe the development of a capacity-strengthening intervention designed for frontline harm reduction workers (FHRWs) to support PrEP awareness-building and promotion among PWUD. METHODS: Our study was implemented in 2 phases in Baltimore City, Maryland. The formative phase focused on a qualitative exploration of the PrEP implementation environment, as well as facilitators and barriers to PrEP willingness and uptake, among cisgender women who use drugs. This work, as well as the existing literature, theory, and feedback from our community partners, informed the intervention development phase, which used an academic-community partnership model. The intervention involved a 1-time, 2-hour training with FHRWs aimed at increasing general PrEP knowledge and developing self-efficacy promoting PrEP in practice (eg, facilitating PrEP dialogues with clients, supporting client advancement along a model of PrEP readiness, and referring clients to PrEP services). In a separate paper, we describe the conduct and results of a mixed methods evaluation to assess changes in PrEP-related knowledge, attitudes, self-efficacy, and promotion practices among FHRWs participating in the training. RESULTS: The pilot was developed from October to December 2021 and implemented from December 2021 through April 2022. We leveraged existing relationships with community-based harm reduction organizations to recruit FHRWs into the intervention. A total of 39 FHRWs from 4 community-based organizations participated in the training across 4 sessions (1 in-person, 2 online synchronous, and 1 online asynchronous). FHRW training attendees represented a diverse range of work cadres, including peer workers, case managers, and organizational administrators. CONCLUSIONS: This intervention could prevent the HIV burden among PWUD by leveraging the relationships that FHRWs have with PWUD and by supporting advancement along the PrEP continuum. Given suboptimal PrEP uptake among PWUD and the limited number of interventions designed to address this gap, our intervention offers an innovative approach to a burgeoning public health problem. If effective, our intervention has the potential to be further developed and scaled up to increase PrEP awareness and uptake among PWUD worldwide.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.152
GPT teacher head0.459
Teacher spread0.307 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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