Leveraging Localized Data to Facilitate the Selection of Evidence-Based Practices to Improve Student Success in Higher Education

Abstract

Health and well-being can no longer be overlooked as a critical factor for student success in higher education. Students perform better when they are well, and institutions of higher education (IHE) have programs and practices that support and encourage student health and well-being. Following an implementation science framework, prior to selection and adoption of evidence-based practices for health and well-being, IHEs must identify the needs of their student population and the specific malleable health risk factors that predict student success, accounting for general health, so that they may effectively select and implement evidence-based programs and practices that lead to supporting student success. Accordingly, I utilized localized health data (N = 1,148) from the most recent National College Health Assessment (NCHA-II, 2018) to understand, which risk factors (stress, poor sleep quality, low physical activity, poor eating habits, psychological distress) predict student success and general health, and for whom does this relationship exist or change. Logistic and ordinal regression analyses were performed. Findings from this study will provide IHE administrators and stakeholders with critical information required for selecting and implementing evidence-based practices for targeted groups of students. This dissertation contributes to the growing body of knowledge on best practices in higher education using commonly collected health data to identify risk factors for student success and campus health disparities. Practice implications and recommendations for future research are discussed.