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

Our findings suggest that transfer learning is a viable method for tracking CEnR studies and provide evidence that the use of fine-tuning strategies significantly improves transformer-based models. Our study also presents a tool for categorizing the type and volume of community engagement in research, which may be useful in addressing the challenges associated with reporting CEnR metrics.