From Conversation to Automation: Leveraging Large Language Models to Analyze Strategies in Problem Solving Therapy

Problem-solving therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues by guiding them through problem identification, solution brainstorming, decision-making, and outcome evaluation. As mental health care increasingly integrates technologies like chatbots and large language models (LLMs), understanding how PST can be effectively automated is important. This study leverages anonymized therapy transcripts to analyze and classify therapeutic interventions using various LLMs and transformer-based models. Our results show that GPT-4o achieved the highest accuracy (0.76) in identifying PST strategies, outperforming other models. Additionally, we introduced a new dimension of communication strategies that enhances the current PST framework, offering deeper insights into therapist-client interactions. This research demonstrates the potential of LLMs to automate complex therapeutic dialogue analysis, providing a scalable, efficient tool for mental health interventions. Our annotation framework can enhance the accessibility, effectiveness, and personalization of PST, supporting therapists in real-time with more precise, targeted interventions.