数字治疗联盟如何影响在线心理健康问答的感知帮助:一个可解释的机器学习方法
Yinghui Huang1,2, Hui Liu3,4, Maomao Chi5
1Research Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan, Hubei Province, China.
Digital health
|May 9, 2025
概括
数字治疗联盟 (DTA) 显著提高了在线心理健康支持中感知到的帮助力. 辅导员的参与促进了DTA,提高了Q&A平台上的响应质量和用户满意度.
科学领域:
- 计算社会科学 计算社会科学
- 心理健康技术 心理健康技术
- 自然语言处理自然语言处理.
背景情况:
- 在线心理健康问答平台对于可访问的支持至关重要.
- 了解影响响应有用性的因素是改善用户体验的关键.
- 数字治疗联盟 (DTA) 是在线心理健康互动的新研究领域.
研究的目的:
- 调查DTA在在线心理健康问答平台上的感知响应有用性的预测和解释作用.
- 确定影响用户感知到帮助性的关键语言和参与因素.
- 开发一个集成DTA和概率模型理论的计算框架.
主要方法:
- 从在线心理健康平台构建了一个由19682个问答互动组成的数据集.
- 采用自然语言处理,可解释的机器学习和因果推理.
- 开发了一种机器学习模型来预测感知到的帮助性.
主要成果:
- 帮助性的预测模型表现出强的表现 (RMSE: 0.8234,MAPE: 22.7288%).
- 辅导员参与 (字数,响应时间) 成为主要预测因素.
- 与DTA相关的线索 (情感纽带,治疗任务) 显著影响了帮助力,辅导员参与增强了帮助力.
结论:
- 这项研究提供了一个计算框架,以了解在线心理健康响应的有用性.
- 调查结果强调了辅导员参与和DTA对感知到的帮助的重要性.
- 结果为平台设计,辅导员培训和用户满意度提供了实用的见解.
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