Kadakia Arya, Preum Sarah Masud, Bohm Andrew R, Fortuna Karen L
BRiTE Center, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, U.S.A.
Department of Computer Science, Dartmouth College, Hanover, U.S.A.
Biomed Eng Syst Technol Int Jt Conf BIOSTEC Revis Sel Pap. 2023 Feb;2023:581-592. doi: 10.5220/0011776500003414.
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.
患有严重精神疾病的成年人受慢性健康状况的影响尤为严重，这些慢性健康状况与医疗和精神疾病管理不善有关，且与不良生活方式行为相关。新兴的干预模式强调同伴专家（为有类似生活经历的人提供情感、社交和实际帮助的认证人员）在促进更好的疾病管理和有意义的社区康复方面的价值。在过去几年中，数字服务和在线平台在传播各种同伴服务方面的使用越来越多。然而，目前的文献无法通过记录所有互动的音频来扩大当前的服务提供方式，以大规模监测和确保保真度。本研究旨在了解数字同伴支持的各个组成部分，以开发一个语料库，并使用自然语言处理对新数据集中同伴支持专家使用的高保真循证技术进行分类。该研究假设，通过对数字同伴支持数据的分析，可以开发出准确率达70%的二元分类器。
