Calibrating a Transformer-Based Model's Confidence on Community-Engaged Research Studies: Decision Support Evaluation Study.

Background: Deep learning offers great benefits in classification tasks such as medical imaging diagnostics or stock trading, especially when compared with human-level performances, and can be a viable option for classifying distinct levels within community-engaged research (CEnR). CEnR is a collaborative approach between academics and community partners with the aim of conducting research that is relevant to community needs while incorporating diverse forms of expertise. In the field of deep learning and artificial intelligence (AI), training multiple models to obtain the highest validation accuracy is common practice; however, it can overfit toward that specific data set and not generalize well to a real-world population, which creates issues of bias and potentially dangerous algorithmic decisions.

Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study.

Background: Community-engaged research (CEnR) involves institutions of higher education collaborating with organizations in their communities to exchange resources and knowledge to benefit a community's well-being. While community engagement is a critical aspect of a university's mission, tracking and reporting CEnR metrics can be challenging, particularly in terms of external community relations and federally funded research programs. In this study, we aimed to develop a method for classifying CEnR studies that have been submitted to our university's institutional review board (IRB) to capture the level of community involvement in research studies.

Attention-Based Models for Classifying Small Data Sets Using Community-Engaged Research Protocols: Classification System Development and Validation Pilot Study.

Background: Community-engaged research (CEnR) is a research approach in which scholars partner with community organizations or individuals with whom they share an interest in the study topic, typically with the goal of supporting that community's well-being. CEnR is well-established in numerous disciplines including the clinical and social sciences. However, universities experience challenges reporting comprehensive CEnR metrics, limiting the development of appropriate CEnR infrastructure and the advancement of relationships with communities, funders, and stakeholders.

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

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.