Facilitators and barriers to providing affirming care for transgender patients in primary care practices in Arkansas.

Purpose: This study assessed factors affecting the provision of affirming-care best practices (ACBPs) for transgender individuals by primary care providers (PCPs) in a rural, southern state METHODS: We conducted a sequential explanatory mixed-methods study in 2020, including a statewide survey (phase 1) and interviews (phase 2). Surveyed PCPs (phase 1) included Medical Doctors/Doctors of Osteopathy (MDs/DOs), nurse practitioners, and 1 physician assistant. Interview participants (phase 2) included providers and staff in 6 practices throughout the state.

Acceptability of a Community Health Worker Program to Link High-Risk People in Jail to HIV Pre-Exposure Prophylaxis.

Background: People involved in the criminal justice system (PICJS) are at a disproportionate risk for HIV infection. Pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention; however, there are barriers to accessing and remaining on PrEP. We explored perspectives on partnering with a community health worker (CHW) to link PICJS to PrEP.

The Couple Relationship Scale: A brief measure to facilitate routine outcome monitoring in couple therapy.

Routine outcome monitoring (ROM) is an important component of evidence-based practice. To implement ROM in their practice, couple therapists need a brief measure that can quickly assess the relevant aspects of a couple's relationship. The Couple Relationship Scale (CRS) is a 10-item measure of relational functioning that assesses emotional intimacy, commitment, trust, safety, cohesion, acceptance, conflict, physical intimacy, overall happiness, and personal well-being.

Interpretation of deep learning in genomics and epigenomics.

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications.