Preliminary Results from a Peer-Led, Social Network Intervention, Augmented by Artificial Intelligence to Prevent HIV among Youth Experiencing Homelessness
Eric Rice, Laura Onasch-Vera, Graham T. DiGuiseppi, Bryan Wilder,, Robin Petering, Chyna Hill, Amulya Yadav, Milind Tambe

TL;DR
This study tests a new AI-driven peer change agent selection method for HIV prevention among homeless youth, showing faster and more effective behavior change compared to traditional methods.
Contribution
It introduces an AI-based PCA selection algorithm that improves the speed and efficacy of HIV prevention interventions among youth experiencing homelessness.
Findings
AI-based PCA selection led to quicker behavior changes.
Both AI and traditional methods improved HIV-related behaviors.
AI intervention effects appeared within 1 month, faster than standard approaches.
Abstract
Each year, there are nearly 4 million youth experiencing homelessness (YEH) in the United States with HIV prevalence ranging from 3 to 11.5%. Peer change agent (PCA) models for HIV prevention have been used successfully in many populations, but there have been notable failures. In recent years, network interventionists have suggested that these failures could be attributed to PCA selection procedures. The change agents themselves who are selected to do the PCA work can often be as important as the messages they convey. To address this concern, we tested a new PCA intervention for YEH, with three arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCA, (2) a popularity arm--the standard PCA approach--operationalized as highest degree centrality (DC), and (3) an observation only comparison group (OBS). PCA models that promote HIV testing, HIV knowledge, and…
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Taxonomy
TopicsHIV, Drug Use, Sexual Risk · Homelessness and Social Issues · COVID-19 epidemiological studies
