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. Tracking studies in which communities are “highly engaged” enables institutions to obtain a more comprehensive understanding of the prevalence of CEnR. OBJECTIVE: We aimed to develop an updated experiment to classify CEnR and capture the distinct levels of involvement that a community partner has in the direction of a research study. To achieve this goal, we used a deep learning–based approach and evaluated the effectiveness of fine-tuning strategies on transformer-based models. METHODS: In this study, we used fine-tuning techniques such as discriminative learning rates and freezing layers to train and test 135 slightly modified classification models based on 3 transformer-based architectures: BERT (Bidirectional Encoder Representations from Transformers), Bio+ClinicalBERT, and XLM-RoBERTa. For the discriminative learning rate technique, we applied different learning rates to different layers of the model, with the aim of providing higher learning rates to layers that are more specialized to the task at hand. For the freezing layers technique, we compared models with different levels of layer freezing, starting with all layers frozen and gradually unfreezing different layer groups. We evaluated the performance of the trained models using a holdout data set to assess their generalizability. RESULTS: Of the models evaluated, Bio+ClinicalBERT performed particularly well, achieving an accuracy of 73.08% and an F(1)-score of 62.94% on the holdout data set. All the models trained in this study outperformed our previous models by 10%-23% in terms of both F(1)-score and accuracy. CONCLUSIONS: 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.