This paper is in the following e-collection/theme issue:

Applications of AI (295) Natural Language Processing (923) Chatbots and Conversational Agents (748) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2145) Mental Health Issues in Adolescence (646)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63701, first published .
Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study

Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study

Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study

Authors of this article:

Silvan Hornstein1 Author Orcid Image ;   Ulrike Lueken1, 2 Author Orcid Image ;   Richard Wundrack3 Author Orcid Image ;   Kevin Hilbert4 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Zhang Z, Fang Z, Du Y, He Y, Qian C, Ye W, Zhang N, Zhang J, Ding X. A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips. Journal of Animal Science and Biotechnology 2025;16(1) View
  2. Higuera-Lozano I, Uusimäki V, Pitkänen T, Denayer E, Dewaele A, Felvinczi K, Goossens L, Kaló Z, Rényi M, Cabello M. Exploring the Characteristics of Online Counseling Chat Services for Youth in Europe: Web Search Study. JMIR Mental Health 2025;12:e67949 View
  3. Curll S, Mazzer K, Albrecht S, Barbic S, Fitzgerald A, Kõlves K, Telford N, Titov N, Rickwood D. Consensus derived client outcomes and clinician actions for youth online chat mental health services: a Delphi study. Frontiers in Digital Health 2025;7 View