Using hidden Markov modelling to reveal in-session stages in text-based counselling

Counselling sessions have multiple stages, each with its themes and objectives. This study aimed to apply Hidden Markov Models (HMMs) to analyse counselling sessions from Open Up, an online text-based counselling platform in Hong Kong. The focus was on inferring latent stages over word distributions...

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Bibliographic Details
Published in:Npj mental health research 2024-11, Vol.3 (1), p.56-10, Article 56
Main Authors: Fu, Ziru, Hsu, Yu Cheng, Chan, Christian S., Liu, Joyce, Yip, Paul S. F.
Format: Article
Language:English
Subjects:
706/689
706/689/477
706/689/477/2811
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Chinese languages
Counseling
Deep learning
Family
Group and Systematic Therapy
Large language models
Medicine
Medicine & Public Health
Mental health
Neurobiology
Probability
Probability distribution
Psychiatry
Psychology
Psychotherapy
Public Health
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Summary:Counselling sessions have multiple stages, each with its themes and objectives. This study aimed to apply Hidden Markov Models (HMMs) to analyse counselling sessions from Open Up, an online text-based counselling platform in Hong Kong. The focus was on inferring latent stages over word distributions and identifying distinctive patterns of progression in more versus less satisfying sessions. Transcripts from 2589 sessions were categorized into more satisfying sessions ( n = 1993 ) and less satisfying sessions ( n = 596 ) based on post-session surveys. A message-level HMM identified five distinct stages: Rapport-building, Problem-identification, Problem-exploration, Problem-solving, and Wrap-up. Compared with less satisfying sessions, more satisfying sessions saw significantly more efficient initial rapport building (7.5% of session duration), problem introduction (20.2%), problem exploration (28.5%), elaborated solution development (46.6%), and concise conclusion (8.2%). This study offers insights for improving the efficiency and satisfaction of text-based counselling services through efficient initial engagement, thorough issue exploration, and focused problem-solving.
ISSN:2731-4251
2731-4251
DOI:10.1038/s44184-024-00103-9