Abstract
Adults with serious mental illnesses are disproportionately affected by chronic health conditions that are linked to inadequately managed medical and psychiatric illnesses and are associated with poor lifestyle behaviors. Emerging intervention models emphasize the value of peer specialists (certified individuals who offer emotional, social, and practical assistance to those with similar lived experiences) in promoting better illness management and meaningful community rehabilitation. Over the last few years, there has been an increasing uptake in the use of digital services and online platforms for the dissemination of various peer services. However, current literature cannot scale current service delivery approaches through audio recording of all interactions to monitor and ensure fidelity at scale. This research aims to understand the individual components of digital peer support to develop a corpus and use natural language processing to classify high-fidelity evidence-based techniques used by peer support specialists in novel datasets. The research hypothesizes that a binary classifier can be developed with an accuracy of 70% through the analysis of digital peer support data.