Investigating the Fidelity of Digital Peer Support: A Preliminary Approach using Natural Language Processing to Scale High-Fidelity Digital Peer Support.

Arya Kadakia, Sarah Masud Preum, Andrew R Bohm, Karen L Fortuna
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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.

调查数字同伴互助的保真度:利用自然语言处理技术扩展高保真数字同伴互助的初步方法。
患有严重精神疾病的成年人受到慢性健康状况的影响尤为严重,这些慢性健康状况与管理不善的医疗和精神疾病有关,并与不良的生活方式行为相关。新出现的干预模式强调了同伴专家(向有类似生活经历的人提供情感、社会和实际帮助的经过认证的个人)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,越来越多的人开始使用数字服务和在线平台来传播各种同伴服务。然而,目前的文献无法通过对所有互动进行录音来监控和确保规模化的忠实性,从而扩展目前的服务提供方法。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技术对同伴支持专家在新数据集中使用的高保真循证技术进行分类。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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