Understanding Client Reactions in Online Mental Health Counseling

38139

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nan

Abstract

AbstractCommunication success relies heavily on reading participants’ reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client’s progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors’ intervention strategies rather than their clients’ reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors’ strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors’ strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients’ states.

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Full Content

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 10358–10376 July 9-14, 2023 ©2023 Association for Computational Linguistics Understanding Client Reactions in Online Mental Health Counseling Anqi Li1,2∗, Lizhi Ma2∗, Yaling Mei2 Hongliang He1,2, Shuai Zhang1,2, Huachuan Qiu1,2, Zhenzhong Lan2† 1 Zhejiang University 2 School of Engineering, Westlake University {lianqi, malizhi, lanzhenzhong}@westlake.edu.cn Abstract Communication success relies heavily on read- ing participants’ reactions. Such feedback is especially important for mental health coun- selors, who must carefully consider the client’s progress and adjust their approach accordingly. However, previous NLP research on counsel- ing has mainly focused on studying counselors’ intervention strategies rather than their clients’ reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encom- passes counselors’ strategies and client reac- tion behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors’ strategies, how such reactions af- fect the final counseling outcomes, and how counselors can adjust their strategies in re- sponse to these reactions. We also demonstrate that this study can help counselors automati- cally predict their clients’ states 1. 1 Introduction There can be no human relations without commu- nication, yet the road to successful communication is paved with obstacles (Luhmann, 1981). Given the individuality and separateness of human con- sciousness, it is hard to guarantee one can receive the message sent by another. Even if the message is fully understood, there can be no assurance of its ac- ceptance. By getting feedback from their partners, communicators can better understand their commu- nicative states. This allows communicators to ad- just their communication strategies to fit better their communication environment, which is crucial for successful communication. However, most work ∗Equal Contribution. †Corresponding Author. 1 You can access our annotation framework, dataset and codes from https://github.com/dll-wu/Client-Reactions. Figure 1: Examples of how counselors adjust their strate- gies according to their client’s reactions. on improving the success rates in communication, such as persuasion (Wang et al., 2019) and men- tal health support (Zhang et al., 2019; Zhang and Danescu-Niculescu-Mizil, 2020; Liu et al., 2021), focuses on speakers’ strategies. But little research is on how listeners’ reactions shape trajectories and outcomes of conversations. In this work, we address the gap by examining how to use the reac- tions of clients to predict and improve the quality of psychological counseling, a field that has profound societal and research impact. Psychological counseling is one of the most chal- lenging and skillful forms of communication (Al- thoff et al., 2016). Counselors take clients through their mental health concerns while balancing the stress they are experiencing (Zhang and Danescu- Niculescu-Mizil, 2020). To do it well, counselors rely on training and on continuing experience with clients to acquire the consultative skills. How- ever, it is difficult for counselors to get direct feed- back on their interventions from clients …

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Labeled Text

AbstractCommunication success relies heavily on reading participants’ reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client’s progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors’ intervention strategies rather than their clients’ reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors’ strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors’ strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients’ states.

Publication Details

Paper ID:
38139
Year:
2023
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan