Accessing trust in mental health counselling conversation

Abstract

Mental health has emerged as the most critical global healthcare concern which is ac celerated by recent lifestyle changes, evolving individual aspirations, and social media. The use of AI in mental health has been useful in every aspect and stage of mental health- from monitoring, assistance and evaluation to providing a cost-effective, scalable, and qualitative improvement of mental health therapeutic outcomes. However, there is a serious lack of research and assistive tools in providing real-time patient feedback to measure aspects and overall therapeutic outcomes of psychotherapy. To this end, we propose a novel metric of trust in mental health dialogue systems. Specifically, every patient’s utterance in a counseling dialogue is scored on a scale that signals the degree of trust developed in the patient towards the therapist at any point in the therapy ses sion. The contribution of this work is twofold (a) we provide a concretized definition of the novel metric of trust in mental health dialogue systems and a clear annotation framework that scores the strength of trust developed between a patient and therapist during counseling. (b) We discuss various strategies to model trust correctly and finally model it as a knowledge-guided time-series forecasting problem. We perform exten sive experimentation and benchmark trust on various classical and modern methods of time-series forecasting, with our best model achieving an MSE loss of 0.0017.