In this section
Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.
@article{Liu:2021:10.3389/fdgth.2021.779091,
author = {Liu, Z and Peach, R and Lawrance, E and Noble, A and Ungless, M and Barahona, M},
doi = {10.3389/fdgth.2021.779091},
journal = {Frontiers in Digital Health},
pages = {1--14},
title = {Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service},
url = {http://dx.doi.org/10.3389/fdgth.2021.779091},
volume = {3},
year = {2021}
}
TY - JOUR
AB - 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.
AU - Liu,Z
AU - Peach,R
AU - Lawrance,E
AU - Noble,A
AU - Ungless,M
AU - Barahona,M
DO - 10.3389/fdgth.2021.779091
EP - 14
PY - 2021///
SN - 2673-253X
SP - 1
TI - Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service
T2 - Frontiers in Digital Health
UR - http://dx.doi.org/10.3389/fdgth.2021.779091
UR - https://www.frontiersin.org/articles/10.3389/fdgth.2021.779091/full
UR - http://hdl.handle.net/10044/1/92967
VL - 3
ER -
If you are interested in meeting with members of the group please contact Marina Evangelou