Gaps in communication theory paradigms when conducting implementation science research: qualitative observations from interviews with administrators, implementors, and evaluators of rural health programs.

Background: Communication is considered an inherent element of nearly every implementation strategy. Often it is seen as a means for imparting new information between stakeholders, representing a Transaction orientation to communication. From a Process orientation, communication is more than information-exchange and is acknowledged as being shaped by (and shaping) the individuals involved and their relationships with one another.

Tracking implementation strategies in real-world settings: VA Office of Rural Health enterprise-wide initiative portfolio.

Objective: To use a practical approach to examining the use of Expert Recommendations for Implementing Change (ERIC) strategies by Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) dimensions for rural health innovations using annual reports on a diverse array of initiatives.

Data Sources And Study Setting: The Veterans Affairs (VA) Office of Rural Health (ORH) funds initiatives designed to support the implementation and spread of innovations and evidence-based programs and practices to improve the health of rural Veterans. This study draws on the annual evaluation reports submitted for fiscal years 2020-2022 from 30 of these enterprise-wide initiatives (EWIs).

Perceptions of an automated benchmarking dashboard for antimicrobial stewardship programs among antimicrobial stewards within the veterans' health administration: a multicenter qualitative study.

Objective: To evaluate the impact of a multicenter, try automated dashboard on ASP activities and its acceptance among ASP leaders.

Design: Frontline stewards were asked to participate in semi-structured interviews before and after implementation of a web-based ASP information dashboard providing risk-adjusted benchmarking, longitudinal trends, and analysis of antimicrobial usage patterns at each facility.

Setting: The study was performed at Iowa City VA Health Care System.

Brain Fog and Fatigue following COVID-19 Infection: An Exploratory Study of Patient Experiences of Long COVID.

Post-acute sequelae of SARS-CoV-2 (PASC) is a poorly understood condition with significant impact on quality of life. We aimed to better understand the lived experiences of patients with PASC, focusing on the impact of cognitive complaints ("brain fog") and fatigue on (1) daily activities, (2) work/employment, and (3) interpersonal relationships. We conducted semi-structured qualitative interviews with 15 patients of a Midwestern academic hospital's post-COVID-19 clinic.

Community Treatment for Opioid Use Disorders in Urban and Rural Veterans.

Purpose: The recent opioid crisis is characterized by a relatively greater increase in opioid use disorder and related mortality in rural populations when compared with urban populations. As almost a quarter of our nation's veterans reside in rural settings, the United States Veterans Health Administration (VHA) is interested in the impact of this epidemic on rural veterans. This study aims to develop a comprehensive understanding of the trends in substance use disorders (SUD) in veterans seeking treatment from community, non-VHA providers.

Racial/Ethnic Differences in Alcohol and Drug Use Outcomes Following Screening, Brief Intervention, and Referral to Treatment (SBIRT) in Federally Qualified Health Centers.

Background: Substance use disorders (SUDs) pose a significant public health concern. Previous findings, while equivocal, demonstrate screening, brief intervention, and referral to treatment (SBIRT) is effective in reducing substance use and improving overall health. While race/ethnic and sex differences in SBIRT outcomes exist, racial/ethnic differences within sex groups remain unclear.

Use of a machine learning framework to predict substance use disorder treatment success.

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem.