Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.

Autor:	Liu Z; Department of Mathematics, Imperial College London, London, United Kingdom., Peach RL; Department of Mathematics, Imperial College London, London, United Kingdom.; Department of Neurology, University Hospital Würzburg, Würzburg, Germany.; Department of Brain Sciences, Imperial College London, London, United Kingdom., Lawrance EL; Institute of Global Health Innovation, Imperial College London, London, United Kingdom.; Mental Health Innovations, London, United Kingdom., Noble A; Mental Health Innovations, London, United Kingdom., Ungless MA; Mental Health Innovations, London, United Kingdom., Barahona M; Department of Mathematics, Imperial College London, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2021 Dec 06; Vol. 3, pp. 779091. Date of Electronic Publication: 2021 Dec 06 (Print Publication: 2021).
DOI:	10.3389/fdgth.2021.779091
Abstrakt:	The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Liu, Peach, Lawrance, Noble, Ungless and Barahona.)
Databáze:	MEDLINE
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