Collaborative Research: A Dyadic and Dynamic Model of Supportive Conversations as Interpersonal Emotion Regulation

几十年的研究表明，人们从社会支持中受益。 那些拥有更丰富的社交网络的人患病率更低，寿命更长，心理健康状况更好。 尽管支持性关系的好处已经很好地建立起来，但人们对这些结果是如何以及为什么发生的知之甚少。 这个项目的重点是人们每天经历的常见压力事件，以及人们与他人的对话，以科普这些事件。 预计有关日常压力源的对话可以帮助人们以不同的方式思考和感受他们的问题，这可能有助于解释常见的短期和长期健康益处。该项目开发了一种编码和分析有关个人问题对话的新方法。 将开发工具来培训外行和专业人士如何在支持性对话中最有帮助，最终使人们的健康和福祉受益。该项目使用了之前进行的四项研究的数据，其中461人向陌生人，朋友或约会伴侣透露了个人压力源。 该项目通过研究谈话者通过语言表达思想和感情的方式，分析谈话者对伴侣信息的反应。 这些对话的录像带和文字记录允许详细编码合作伙伴的每一句话的反应。 编码系统能够区分在这种对话中经常出现的许多不同的语句和表达。 对这些对话的详细分析将阐明支持性信息对痛苦者的思想和感受的影响。 这些分析还将阐明对话中的动态如何影响痛苦者的思想和情绪。 这个项目将有助于未来的研究，旨在开发机器编码系统的分类和量化方面制定的社会支持，同时也汇编数据分析工具，使研究人员能够调查动态的二元互动更普遍。这个奖项反映了NSF的法定使命，并已被认为是值得支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Decades of research shows that people benefit from social support. Those with richer social networks experience lower rates of disease, live longer lives, and enjoy better mental health. Even though the benefits of supportive relationships are well established, less is known about how and why those outcomes occur. This project focuses on common stressful events that people experience every day, and the conversations people have with others to cope with those events. It is expected that conversations about everyday stressors help people think and feel differently about their problems, which may help account for the short- and long-term health benefits so commonly found. This project develops a new method for the coding and analysis of conversations about personal problems. Tools will be developed to train laypersons and professionals in how to be most helpful in supportive conversations, ultimately benefiting people's health and well-being.This project uses data from four previously conducted studies in which 461 people disclosed a personal stressor to a stranger, friend, or dating partner. The project analyzes the discloser's reactions to the partner's messages by examining the ways in which the discloser expresses thoughts and feelings through language. Videotapes and transcripts of these conversations allow detailed coding of every utterance of the partner's responses. The coding system is capable of distinguishing among many different statements and expressions that commonly occur in such conversations. Detailed analyses of these conversations will illuminate the turn-to-turn effects of supportive messages on a distressed person's thoughts and feelings. The analyses will also clarify how dynamics within conversations influence the distressed person's thoughts and emotions. This project will contribute to future research aimed at developing machine-coding systems for categorizing and quantifying aspects of enacted social support, while also compiling data-analytical tools that will enable researchers to investigate the dynamics of dyadic interaction more generally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.