Developing a classification system and algorithm to track community-engaged research using IRB protocols at a large research university

Abstract

Community-engaged research (CEnR) is now an established research approach. The current research seeks to pilot the systematic and automated identification and categorization of CEnR to facilitate longitudinal tracking using administrative data. We inductively analyzed and manually coded a sample of Institutional Review Board (IRB) protocols. Comparing the variety of partnered relationships in practice with established conceptual classification systems, we developed five categories of partnership: Non-CEnR, Instrumental, Academic-led, Cooperative, and Reciprocal. The coded protocols were used to train a deep-learning algorithm using natural language processing to categorize research. We compared the results to data from three questions added to the IRB application to identify whether studies had a community partner and the type of engagement planned. The preliminary results show that the algorithm is potentially more likely to categorize studies as CEnR compared to investigator-recorded data and to categorize studies at a higher level of engagement. With this approach, universities could use administrative data to inform strategic planning, address progress in meeting community needs, and coordinate efforts across programs and departments. As scholars and technical experts improve the algorithm’s accuracy, universities and research institutions could implement standardized reporting features to track broader trends and accomplishments.

Keywords

• Community engagement
• natural language processing
• tracking
• classification
• community-engaged research