Detecting premature departure in online text-based counseling using logic-based pattern matching

Background: More so than face-to-face counseling, users of online text-based services might drop out from a session before establishing a clear closure or expressing the intention to leave. Such premature departure may be indicative of heightened risk or dissatisfaction with the service or counselor...

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Autores principales:	Yucan Xu, Christian S. Chan, Christy Tsang, Florence Cheung, Evangeline Chan, Jerry Fung, James Chow, Lihong He, Zhongzhi Xu, Paul S.F. Yip
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	E-counseling Text-based counseling Dropouts Pattern matching Text matching Information technology T58.5-58.64 Psychology BF1-990
Acceso en línea:	https://doaj.org/article/0fe884e474984a2a978ddfbed61ae2bb
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spelling	oai:doaj.org-article:0fe884e474984a2a978ddfbed61ae2bb2021-11-26T04:29:53ZDetecting premature departure in online text-based counseling using logic-based pattern matching2214-782910.1016/j.invent.2021.100486https://doaj.org/article/0fe884e474984a2a978ddfbed61ae2bb2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2214782921001263https://doaj.org/toc/2214-7829Background: More so than face-to-face counseling, users of online text-based services might drop out from a session before establishing a clear closure or expressing the intention to leave. Such premature departure may be indicative of heightened risk or dissatisfaction with the service or counselor. However, there is no systematic way to identify this understudied phenomenon. Purpose: This study has two objectives. First, we developed a set of rules and used logic-based pattern matching techniques to systematically identify premature departures in an online text-based counseling service. Second, we validated the importance of premature departure by examining its association with user satisfaction. We hypothesized that the users who rated the session as less helpful were more likely to have departed prematurely. Method: We developed and tested a classification model using a sample of 575 human-annotated sessions from an online text-based counseling platform. We used 80% of the dataset to train and develop the model and 20% of the dataset to evaluate the model performance. We further applied the model to the full dataset (34,821 sessions). We compared user satisfaction between premature departure and completed sessions based on data from a post-session survey. Results: The resulting model achieved 97% and 92% F1 score in detecting premature departure cases in the training and test sets, respectively, suggesting it is highly consistent with the judgment of human coders. When applied to the full dataset, the model classified 15,150 (43.5%) sessions as premature departure and the remaining 19,671 (56.5%) as completed sessions. Completed cases (15.2%) were more likely to fill the post-chat survey than premature departure cases (4.0%). Premature departure was significantly associated with lower perceived helpfulness and effectiveness in distress reduction. Conclusions: The model is the first that systematically and accurately identifies premature departure in online text-based counseling. It can be readily modified and transferred to other contexts for the purpose of risk mitigation and service evaluation and improvement.Yucan XuChristian S. ChanChristy TsangFlorence CheungEvangeline ChanJerry FungJames ChowLihong HeZhongzhi XuPaul S.F. YipElsevierarticleE-counselingText-based counselingDropoutsPattern matchingText matchingInformation technologyT58.5-58.64PsychologyBF1-990ENInternet Interventions, Vol 26, Iss , Pp 100486- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	E-counseling Text-based counseling Dropouts Pattern matching Text matching Information technology T58.5-58.64 Psychology BF1-990
spellingShingle	E-counseling Text-based counseling Dropouts Pattern matching Text matching Information technology T58.5-58.64 Psychology BF1-990 Yucan Xu Christian S. Chan Christy Tsang Florence Cheung Evangeline Chan Jerry Fung James Chow Lihong He Zhongzhi Xu Paul S.F. Yip Detecting premature departure in online text-based counseling using logic-based pattern matching
description	Background: More so than face-to-face counseling, users of online text-based services might drop out from a session before establishing a clear closure or expressing the intention to leave. Such premature departure may be indicative of heightened risk or dissatisfaction with the service or counselor. However, there is no systematic way to identify this understudied phenomenon. Purpose: This study has two objectives. First, we developed a set of rules and used logic-based pattern matching techniques to systematically identify premature departures in an online text-based counseling service. Second, we validated the importance of premature departure by examining its association with user satisfaction. We hypothesized that the users who rated the session as less helpful were more likely to have departed prematurely. Method: We developed and tested a classification model using a sample of 575 human-annotated sessions from an online text-based counseling platform. We used 80% of the dataset to train and develop the model and 20% of the dataset to evaluate the model performance. We further applied the model to the full dataset (34,821 sessions). We compared user satisfaction between premature departure and completed sessions based on data from a post-session survey. Results: The resulting model achieved 97% and 92% F1 score in detecting premature departure cases in the training and test sets, respectively, suggesting it is highly consistent with the judgment of human coders. When applied to the full dataset, the model classified 15,150 (43.5%) sessions as premature departure and the remaining 19,671 (56.5%) as completed sessions. Completed cases (15.2%) were more likely to fill the post-chat survey than premature departure cases (4.0%). Premature departure was significantly associated with lower perceived helpfulness and effectiveness in distress reduction. Conclusions: The model is the first that systematically and accurately identifies premature departure in online text-based counseling. It can be readily modified and transferred to other contexts for the purpose of risk mitigation and service evaluation and improvement.
format	article
author	Yucan Xu Christian S. Chan Christy Tsang Florence Cheung Evangeline Chan Jerry Fung James Chow Lihong He Zhongzhi Xu Paul S.F. Yip
author_facet	Yucan Xu Christian S. Chan Christy Tsang Florence Cheung Evangeline Chan Jerry Fung James Chow Lihong He Zhongzhi Xu Paul S.F. Yip
author_sort	Yucan Xu
title	Detecting premature departure in online text-based counseling using logic-based pattern matching
title_short	Detecting premature departure in online text-based counseling using logic-based pattern matching
title_full	Detecting premature departure in online text-based counseling using logic-based pattern matching
title_fullStr	Detecting premature departure in online text-based counseling using logic-based pattern matching
title_full_unstemmed	Detecting premature departure in online text-based counseling using logic-based pattern matching
title_sort	detecting premature departure in online text-based counseling using logic-based pattern matching
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0fe884e474984a2a978ddfbed61ae2bb
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_version_	1718409823642451968

Detecting premature departure in online text-based counseling using logic-based pattern matching

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