共同对抗脱发:探索在线脱发支持社区中的自我披露和社会支持

Zizhong Zhang
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引用次数: 0

摘要

目的 脱发常常被人忽视,但在心理上却具有挑战性。然而,网上健康社区的出现为脱发患者提供了通过自我披露寻求社会支持的机会。然而,并不是所有的披露都能得到所期望的支持。本研究探讨了脱发患者在社区中披露的内容,以及他们关于自我披露的健康叙述(内容、形式和语言风格)如何影响他们获得的社会支持。研究采用结构主题模型、语言学探究和字数统计以及负二项模型,分析了自我披露的内容以及社会支持与自我披露的三个叙事维度之间的相互关系。与情感相关的自我披露,无论是在内容上还是在有效用词上,都能获得更深层次的社会支持。篇幅较长、图片丰富的帖子在数量上吸引了更多的支持,但在质量上不一定,而认知性词语的影响有限。 原创性/价值 本研究针对的是以前在网络健康社区中被忽视的脱发患者群体。它采用了一个更全面的健康叙事框架来探索自我披露与社会支持之间的关系,并利用无监督结构主题建模方法来挖掘文本。这项研究对患者如何寻求支持以及医疗保健专业人员如何制定医患沟通策略具有实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fight against hair loss together: exploring self-disclosure and social support in an online hair loss support community
Purpose Hair loss is often overlooked but psychologically challenging. However, the emergence of online health communities provides opportunities for hair loss patients to seek social support through self-disclosure. Nevertheless, not all disclosures receive the desired support. This research explores what patients disclose within the community and how their health narrative (content, form and linguistic style) regarding self-disclosure influences the social support they receive.Design/methodology/approachThis study investigated a 13-year-old online support group for Chinese hair loss patients with nearly 240,000 members. Using structural topic modeling, Linguistic Inquiry and Word Count, and a negative binomial model, the research analyzed the content of self-disclosure and the interrelationships between social support and three narrative dimensions of self-disclosure.FindingsSelf-disclosures are classified into 14 topics, grouped under analytical, informative and emotional categories. Emotion-related self-disclosures, whether in content or effective word use, receive deeper social support. Longer and image-rich posts attract more support in quantity, but not necessarily in quality, while cognitive words have a limited impact.Originality/valueThis study addresses the previously overlooked population of hair loss patients within online health communities. It employs a more comprehensive health narrative framework to explore the relationship between self-disclosure and social support, utilizing unsupervised structural topic modeling methods to mine text. The research offers practical implications for how patients seek support and for healthcare professionals in developing doctor-patient communication strategies.
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