Research discoveries often take years to reach routine practice. Implementation science seeks to understand and address this gap by studying how evidence-based interventions can be adopted, implemented, and sustained in real-world settings. In this introductory workshop, Dr. Reisinger will provide an overview of the core concepts, questions, and methods of implementation science, including implementation frameworks, strategies, and outcomes. Participants will also learn about resources available through the University of Iowa Implementation Science Center to support implementation science research, training, and collaboration.

This workshop is an introduction to social network analysis. Participants will discuss core concepts (e.g., density and transitivity) as well as descriptive and inferential techniques, such as community detection, that can be used to understand and analyze networks. All coding will be done in R. Familiarity with R is encouraged but not required to participate in the workshop.

This workshop introduces directed acyclic graphs (DAGs) as a practical tool for selecting variables to include in analyses. Participants will learn how DAGs can help identify potential confounding and support transparent analytic decisions. Using examples from epidemiological research, the workshop will demonstrate how to develop and interpret DAGs, apply them to covariate selection, and perform sensitivity analyses to evaluate the robustness of the covariate selection process. This workshop is intended for students, researchers, and public health professionals with a basic understanding of regression modeling. No prior experience with DAGs or causal inference methods is required.