Understanding Client Reactions in Online Mental Health Counseling

• URL: http://arxiv.org/abs/2306.15334v1
• Date: Tue, 27 Jun 2023 09:39:54 GMT
• Title: Understanding Client Reactions in Online Mental Health Counseling
• Authors: Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang, Huachuan Qiu, Zhenzhong Lan
• Abstract summary: 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. Our study shows 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.
• Score: 11.12088513389927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Communication 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 shows 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.

Related papers

• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory [24.937025825501998]
  We create a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT) We benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations. Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.
  arXiv  Detail & Related papers  (2024-07-03T13:41:31Z)
• Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors [43.42054421125617]
  Existing mechanisms of providing feedback largely rely on human supervision. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors.
  arXiv  Detail & Related papers  (2024-03-21T04:23:56Z)
• Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs [18.605352662843575]
  We present an automatic approach using large language models (LLMs) to understand the development of therapeutic alliance in text-based counseling. We collect a comprehensive counseling dataset and conduct multiple expert evaluations on a subset based on this framework. Our findings underscore the challenges counselors face in cultivating strong online relationships with clients.
  arXiv  Detail & Related papers  (2024-02-19T09:00:10Z)
• K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning [83.19215082550163]
  We propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q&A forum.
  arXiv  Detail & Related papers  (2023-12-16T08:10:10Z)
• PsyChat: A Client-Centric Dialogue System for Mental Health Support [16.008761874266728]
  PsyChat is a client-centric dialogue system that provides psychological support through online chat. It comprises five modules: client behavior recognition, counselor strategy selection, input packer, response generator, and response selection. Case study demonstrates that the dialogue system can predict the client's behaviors, select appropriate counselor strategies, and generate accurate and suitable responses.
  arXiv  Detail & Related papers  (2023-12-07T12:40:00Z)
• Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach [58.88422314998018]
  Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies which ignore the effect on ES and lack explicit goals to guide emotional positive transition. We introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
  arXiv  Detail & Related papers  (2023-07-16T09:58:44Z)
• Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback [40.065280357381035]
  CARE is an interactive AI-based tool to empower peer counselors through automatic suggestion generation. During the practical training stage, CARE helps diagnose which specific counseling strategies are most suitable in the given context. CARE especially helps novice counselors respond better in challenging situations.
  arXiv  Detail & Related papers  (2023-05-15T19:48:59Z)
• Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform [35.9642101732025]
  This paper seeks to bridge the gap by mapping peer-counselor chat-messages to motivational interviewing techniques. We study the impact of MI techniques on the conversation ratings to investigate the techniques that predict clients' satisfaction with their counseling sessions. This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms.
  arXiv  Detail & Related papers  (2022-11-09T20:25:33Z)
• Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework [51.237191651923666]
  We investigate the potential of agent learning from trainers' facial expressions via interpreting them as evaluative feedback. With designed CNN-RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback. Our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible.
  arXiv  Detail & Related papers  (2020-01-23T17:50:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.