125 Thoughts on Designing a Summer Internship for Undergraduates at an Academic Medical Center

OBJECTIVES/GOALS: The overall goal is to provide tips and suggestions for designing a summer internship program for undergraduates at an institution that only offers advanced degrees. This poster seeks to highlight what worked well and items that can be improved upon from a program that ran for the first time at the University of Texas Medical Branch in Summer 2022. METHODS/STUDY POPULATION: In February 2022, we opened admissions to a summer internship program whose goals are to expose students to statistical design and analysis problems from real research, while providing them with the conceptual and technical tools to address these problems. As such, students should be able 1) to assess study designs for strengths and weaknesses in addressing specific biomedical questions, 2) to use the tools of statistical modeling, data science, and hypothesis testing along with state-of-the-art statistical software to work on problems as well as produce results and conclusions for these problems, and 3) to understand how developing these skills can lead to a wide variety of career opportunities both inside and outside of academia. We ultimately recruited 12 participants in the first summer. RESULTS/ANTICIPATED RESULTS: Based on feedback from participants, we have identified recruitment efforts that worked (and those that need improvement), teaching methods that worked (as well as those that need improving), and ideas for career development sessions (based on questions generated from current participants). Each participant was paired with another student on a research project. Each research project had both a biostatistics faculty mentor as well as a basic science/clinical faculty mentor. We will discuss what worked/what needs improvement from this sort of situation as well. The program culminated in a poster session which was very well received by participants and faculty alike. DISCUSSION/SIGNIFICANCE: This internship program is significant because developing a workforce knowledgeable in biostatistics and data science has been increasingly in demand to improve the efficiency and reliability of biomedical innovation. My providing guidance to others hoping to provide similar programs, we aim to develop such skills in the incoming workforce.