在基于文本的咨询中评估情绪变化的异质性:隐藏类轨迹分析
Ziru Fu1, Yu Cheng Hsu2, Christian Shaunlyn Chan3
1Department of Social Work and Social Administration, Faculty of Social Sciences, Faculty of Social Sciences, University of Hong Kong, Hong Kong, China (Hong Kong).
Journal of medical Internet research
|September 5, 2025
概括
研究人员在网上咨询中发现了三种情绪轨迹:稳定改善,恶化和反弹. 这些模式的早期识别可以增强基于文本的心理健康支持.
科学领域:
- 数字心理健康
- 计算社会科学
- 临床心理学
背景情况:
- 基于文本的在线辅导正在增长,但其性质在监测用户情绪转变方面存在挑战.
- 匿名性和文本格式阻碍了个性化干预,可能会影响服务的有效性和用户满意度.
研究的目的:
- 在线基于文本的辅导中识别寻求帮助者的独特的会议内情感轨迹.
- 检查与不同情绪轨迹成员关系的关键变量.
主要方法:
- 在6207个辅导会议中使用增长混合模型 (GMM) 进行潜在类轨迹分析.
- 通过ChatGPT标记的求助信息的情绪评分被用于轨迹建模.
- 多项逻辑回归确定了与类成员关系相关的变量.
主要成果:
- 三个不同的情绪轨迹被确定:稳定改善 (18.9%),恶化 (18.0%),然后反弹 (63.1%).
- 病情恶化的轨迹与自杀念头,家庭/身体健康问题,及早结束治疗相关.
结论:
- 在基于文本的在线辅导中存在不同的会议内情感轨迹.
- 早期识别这些轨迹可以使辅导员能够调整干预措施,提高数字心理健康服务的有效性和满意度.
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