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

几十年的研究表明，人们受益于社会支持。那些拥有更丰富的社交网络的人罹患疾病的几率更低，寿命更长，精神健康状况更好。尽管支持性关系的好处已经确立，但人们对这些结果是如何发生的以及为什么会发生却知之甚少。这个项目专注于人们每天经历的常见压力事件，以及人们为应对这些事件而与他人进行的对话。关于日常压力源的对话有望帮助人们以不同的方式思考和感受他们的问题，这可能有助于解释人们通常发现的短期和长期健康益处。这个项目开发了一种新的方法来编码和分析关于个人问题的对话。将开发工具来培训外行人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，在这些研究中，461人向陌生人、朋友或约会伙伴透露了个人压力源。该项目通过检查泄密者通过语言表达思想和感受的方式来分析泄密者对合作伙伴信息的反应。这些对话的录像带和文字记录可以对伴侣的每一句回应进行详细的编码。该编码系统能够区分在这种对话中常见的许多不同的陈述和表达。对这些对话的详细分析将阐明支持性信息对痛苦的人的想法和感觉的轮流影响。这些分析还将阐明对话中的动态如何影响痛苦的人的思想和情绪。该项目将有助于未来的研究，旨在开发机器编码系统，对已制定的社会支持的各个方面进行分类和量化，同时还将汇编数据分析工具，使研究人员能够更广泛地调查二元互动的动态。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.