Detecting climate anxiety in therapy through natural language processing.
Climate change
Climate-aware therapy
Eco-anxiety
Machine learning
Psychotherapy
Real-world evidence
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 10 2024
29 10 2024
Historique:
received:
10
02
2024
accepted:
03
10
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39472482
doi: 10.1038/s41598-024-75269-5
pii: 10.1038/s41598-024-75269-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25976Informations de copyright
© 2024. The Author(s).
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