Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

Background: Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major driver of new infections. People who use web-based venues to meet sex partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections. To our knowledge, no machine learning (ML) interventions have been developed that use web-based and digital technologies to inform and personalize HIV and substance use prevention efforts for SGM young people.

Strengths and challenges among Black and Latinx people living with HIV during COVID-19: A mixed-methods investigation of the translation of self-management across syndemic health crises.

Black and Latinx people are disproportionately impacted by HIV, COVID-19, and other syndemic health crises with similar underlying social determinants of health. Lessons learned from the HIV pandemic and COVID-19 response have been invoked to improve health equity at the systemic level in the face of other emergent health crises. However, few have examined the potential translation of strategies between syndemics at the individual level.

Challenging dominant narratives, interrupting objectification, and queer creativity: Queer sex worker art in Los Angeles.

Drawing on queer of color critique, this paper uses mixed methods including participant observation, interviews, visual and textual analysis, and photovoice to interrogate sex workers' queer creative practices. Building upon the larger oeuvre of sex working artists, contemporary sex working artists in Los Angeles utilizes queer creativity to thwart hegemonic readings of sex work. Performances at two activist fundraisers drew on the themes and esthetics of sex work to counter mainstream narratives about sex work and workers, and to interrupt their objectification.