An official website of the United States government
Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with mental health issues who use peer counseling. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors, especially novices, at scale. To achieve this, we co-design with a group of senior psychotherapy supervisors to develop a multi-level feedback taxonomy, and then construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback. Via qualitative and quantitative evaluation with domain experts, we demonstrate that our method minimizes the risk of potentially harmful and low-quality feedback generation which is desirable in such high-stakes scenarios.
more »
« less
This content will become publicly available on December 30, 2025
Evaluating Language Models for Assessing Counselor Reflections
Reflective listening is a fundamental communication skill in behavioral health counseling. It enables counselors to demonstrate an understanding of and empathy for clients’ experiences and concerns. Training to acquire and refine reflective listening skills is essential for counseling proficiency. Yet, it faces significant barriers, notably the need for specialized and timely feedback to improve counseling skills. In this work, we evaluate and compare several computational models, including transformer-based architectures, for their ability to assess the quality of counselors’ reflective listening skills. We explore a spectrum of neural-based models, ranging from compact, specialized RoBERTa models to advanced large-scale language models such as Flan, Mistral, and GPT-3.5, to score psychotherapy reflections. We introduce a psychotherapy dataset that encompasses three basic levels of reflective listening skills. Through comparative experiments, we show that a finetuned small RoBERTa model with a custom learning objective (Prompt-Aware margIn Ranking (PAIR)) effectively provides constructive feedback to counselors in training. This study also highlights the potential of machine learning in enhancing the training process for motivational interviewing (MI) by offering scalable and effective feedback alternatives for counseling training.
more »
« less
- Award ID(s):
- 2306372
- PAR ID:
- 10616293
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Computing for Healthcare
- ISSN:
- 2637-8051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Online peer-to-peer therapy sessions can be effective in improving people's mental well-being. However, online volunteer counselors may lack the expertise and necessary training to provide high-quality sessions, and these low-quality sessions may negatively impact volunteers' motivations as well as clients' well-being. This paper uses interviews with 20 senior online volunteer counselors to examine how they addressed challenges and acquired skills when volunteering in a large, mental-health support community - 7Cups.com. Although volunteers in this community received some training based on principles of active listening and motivational interviewing, results indicate that the training was insufficient and that volunteer counselors had to independently develop strategies to deal with specific challenges that they encountered in their volunteer work. Their strategies, however, might deviate from standard practice since they generally lacked systematic feedback from mentors or clients and, instead, relied on their personal experiences. Additionally, volunteer counselors reported having difficulty maintaining their professional boundaries with the clients. Even though training and support resources were available, they were underutilized. The results of this study have uncovered new design spaces for HCI practitioners and researchers, including social computing and artificial intelligence approaches that may provide better support to volunteer counselors in online mental health communities.more » « less
-
Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.more » « less
-
Millions of users come to online peer counseling platforms to seek support on diverse topics ranging from relationship stress to anxiety. However, studies show that online peer support groups are not always as effective as expected largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most of them often do not have systematic ways to receive guidelines or supervision. In this work, we introduce CARE: 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 and provides tailored example responses as suggestions. Counselors can choose to select, modify, or ignore any suggestion before replying to the support seeker. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data together with advanced natural language generation techniques to achieve these functionalities. We demonstrate the efficacy of CARE by performing both quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews. We also find that CARE especially helps novice counselors respond better in challenging situations.more » « less
-
Ensuring the effectiveness of text-based crisis counseling requires observing ongoing conversations and providing feedback, both labor-intensive tasks. Automatic analysis of conversations—at the full chat and utterance levels—may help support counselors and provide better care. While some session-level training data (e.g., rating of patient risk) is often available from counselors, labeling utterances requires expensive post hoc annotation. But the latter can not only provide insights about conversation dynamics, but can also serve to support quality assurance efforts for counselors. In this paper, we examine if inexpensive—and potentially noisy—session-level annotation can help improve label utterances. To this end, we propose a logic-based indirect supervision approach that exploits declaratively stated structural dependencies between both levels of annotation to improve utterance modeling. We show that adding these rules gives an improvement of 3.5% f-score over a strong multi-task baseline for utterance-level predictions. We demonstrate via ablation studies how indirect supervision via logic rules also improves the consistency and robustness of the system.more » « less
