181 Mapping Translational Research Collaborations: Insights from an IDeA Clinical and Translational Research Center

Abstract

OBJECTIVES/GOALS: Policy makers are interested in understanding scientific collaborations that translate knowledge into population health. The objective of this study is to compare the translational research collaboration of the Hispanic Alliance of Clinical and Translational Research in 2020 and 2023 by using Social Network Analysis (SNA). METHODS/STUDY POPULATION: We conducted a systematic document review of all the Hispanic Alliance Calls for Pilot Projects from 2020 to 2023 including key attributes of the investigators and collaborators such as academic institution, highest degree, and collaborator type. Scientific collaboration was defined as two or more researchers working together in grant proposal for a pilot project application. Study data was recorded and tracked using an Excel spreadsheet. R Statistical software was used to analyze and map the networks resulting from collaboration interactions comparing the 2020 Call and 2023 Call. Network statistics were performed including nodes, isolates, edges, components, density, diameter, average degree, and the size of the main component. RESULTS/ANTICIPATED RESULTS: A total of 134 investigators comprised the overall network. The network are predominantly clinician (49.3%) and basic researchers (25.4%). Preliminary results shows that diversity of disciplines and affiliations in the collaborative relationships increased across time. Findings demonstrated that the number of nodes/actors increased from 16 to 65 comparing 2020 to 2023 and the edges/relationships from 12 to 53. The number of translational research cluster increased from 4 to 13 comparing 2020 to 2023. More extensive collaborative cluster occurred across time with over 15 researchers collaborating. A mentor was the key player connecting these research clusters. DISCUSSION/SIGNIFICANCE: This study provides critical data to mapping the IDeA CTR translational research collaboration patterns. Research collaboration increased across time. This innovative approach serves to foster data-driven decision-making to enhance collaboration, diversity, and program outcomes. It offers valuable insights for policy and practice.