A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

Abstract

Religious or spiritual struggles are clinically important to health care chaplains because they are related to poorer health outcomes, involving both mental and physical health problems. Identifying persons experiencing religious struggle poses a challenge for chaplains. One potentially underappreciated means of triaging chaplaincy effort are prayers written in chapel notebooks. We show that religious struggle can be identified in these notebooks through instances of negative religious coping, such as feeling anger or abandonment toward God. We built a data set of entries in chapel notebooks and classified them as showing religious struggle, or not. We show that natural language processing techniques can be used to automatically classify the entries with respect to whether or not they reflect religious struggle with as much accuracy as humans. The work has potential applications to triaging chapel notebook entries for further attention from pastoral care staff.