Detecting climate anxiety in therapy through natural language processing

Abstract

Abstract A well-documented consequence of global warming is increased psychological distress and climate anxiety, but data gaps limit action. While climate anxiety garners attention, its expression in therapy remains unexplored. Natural language processing (NLP) models can identify climate discussions in therapy, aiding therapists and informing training. This study analyzed 32,542 therapy sessions provided by 849 therapists to 7,916 clients in U.S. behavioral health programs between July 2020 and December 2022, yielding 1,722,273 labeled therapist-client micro-dialogues. Climate- and weather-related topics constituted a mere 0.3% of the sessions. Clients exhibiting higher levels of depressive or anxiety symptoms were less likely to discuss weather and climate compared to those with mild or no symptoms. Findings suggest that although global warming is known to impact mental health, these issues are not yet adequately addressed in psychotherapy. This study suggests a potential gap between the documented mental health concerns associated with climate change and their representation in psychotherapy. NLP models can provide valuable feedback to therapists and assist in identifying key moments and conversational topics to inform training and improve the effectiveness of therapy sessions.

Keywords

• Climate-aware therapy
• Climate change
• Eco-anxiety
• Machine learning
• Real-world evidence
• Psychotherapy