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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.
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Psychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches
There are many different forms of psychotherapy. Itemized inventories of psychotherapeutic interventions provide a mechanism for evaluating the quality of care received by clients and for conducting research on how psychotherapy helps. However, evaluations such as these are slow, expensive, and are rarely used outside of well-funded research studies. Natural language processing research has progressed to allow automating such tasks. Yet, NLP work in this area has been restricted to evaluating a single approach to treatment, when prior research indicates therapists used a wide variety of interventions with their clients, often in the same session. In this paper, we frame this scenario as a multi-label classification task, and develop a group of models aimed at predicting a wide variety of therapist talk-turn level orientations. Our models achieve F1 macro scores of 0.5, with the class F1 ranging from 0.36 to 0.67. We present analyses which offer insights into the capability of such models to capture psychotherapy approaches, and which may complement human judgment.
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- Award ID(s):
- 1822877
- PAR ID:
- 10357676
- Date Published:
- Journal Name:
- Eighth Workshop on Computational Linguistics and Clinical Psychology
- Page Range / eLocation ID:
- 47 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2022.clpsych-1.5
