Fu Ziru, Hsu Yu Cheng, Chan Christian S, Liu Joyce, Yip Paul S F
The Hong Kong Jockey Club Centre for Suicide Research and Prevention, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China.
Department of Psychology and Linguistics, International Christian University, Tokyo, Japan.
Npj Ment Health Res. 2024 Nov 21;3(1):56. doi: 10.1038/s44184-024-00103-9.
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 ( ) and less satisfying sessions ( ) 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.
咨询会话有多个阶段，每个阶段都有其主题和目标。本研究旨在应用隐马尔可夫模型（HMM）来分析来自香港一个基于文本的在线咨询平台“敞开心扉”的咨询会话。重点是根据单词分布推断潜在阶段，并识别在满意度较高和较低的会话中不同的进展模式。根据会话后的调查，来自2589个会话的记录被分为满意度较高的会话（ ）和满意度较低的会话（ ）。一个消息级的HMM识别出五个不同阶段：建立融洽关系、问题识别、问题探索、解决问题和总结。与满意度较低的会话相比，满意度较高的会话在建立初始融洽关系（占会话时长的7.5%）、问题引入（20.2%）、问题探索（28.5%）、详细解决方案制定（46.6%）和简洁结论（8.2%）方面效率显著更高。本研究通过高效的初始参与、深入的问题探索和有针对性的问题解决，为提高基于文本的咨询服务的效率和满意度提供了见解。
