通过人机语言模型交互促进自助式心理健康干预：以认知重构为例的案例研究
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring
AI总结：
本研究以认知重构为案例，设计了基于语言模型的自助心理健康干预辅助系统，经15531人参与的随机实地研究验证其有效，且定制化简化生成内容可提升效果与公平性。
摘要：
自助式心理健康干预（例如用于学习和练习应对策略的“DIY”工具）在提升心理健康服务可及性方面展现出巨大潜力。然而，这类干预通常对认知能力要求较高，且容易引发情绪波动，形成了可及性障碍，限制了其大规模推广与应用。本文研究了人类与语言模型的交互如何为自助式心理健康干预提供支持，并以认知重构（一种经实证验证的、用于克服消极思维的治疗技术）作为案例展开研究。在经IRB（机构审查委员会）批准、于某大型心理健康网站开展的随机实地研究中，我们招募了15531名参与者，设计并评估了一套利用语言模型辅助用户完成认知重构各步骤的系统。研究结果显示，该系统对67%的参与者的情绪强度产生了积极影响，且帮助65%的参与者克服了消极想法。尽管青少年群体的干预效果相对较差，但我们发现，通过简化语言模型生成内容的定制化干预方案，能够提升整体干预效果与公平性。
英文摘要：
Self-guided mental health interventions, such as "do-it-yourself" tools to learn and practice coping strategies, show great promise to improve access to mental health care. However, these interventions are often cognitively demanding and emotionally triggering, creating accessibility barriers that limit their wide-scale implementation and adoption. In this paper, we study how human-language model interaction can support self-guided mental health interventions. We take cognitive restructuring, an evidence-based therapeutic technique to overcome negative thinking, as a case study. In an IRB-approved randomized field study on a large mental health website with 15,531 participants, we design and evaluate a system that uses language models to support people through various steps of cognitive restructuring. Our findings reveal that our system positively impacts emotional intensity for 67% of participants and helps 65% overcome negative thoughts. Although adolescents report relatively worse outcomes, we find that tailored interventions that simplify language model generations improve overall effectiveness and equity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
