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Abstract：
Causes of and treatments for long-COVID syndrome remain unknown. Drawing on uncertainty management theory (UMT), this study elucidates the communicative nature of crowdsourced medicine as a means by which COVID \"long-haulers\" respond to their poorly understood illness.
31,892 posts on the long-haulers subreddit (r/covidlonghaulers) were analyzed, starting with its creation date, July 24th, 2020, until January 7, 2021. The Meaning Extraction Method was used to identify clusters of words that mathematically group together across the text observations.
Analyses yielded 16 distinct factors of words, which we thematized based on their composition, the data, and UMT. The 16 themes encompassed symptoms (e.g., pain, respiratory, sensory), diagnostic concerns (testing, diagnosis), broad health concerns (immunity, physical activity, diet), chronicity, support, identity, and anxiety.
Findings provide a succinct, yet robust set of themes reflecting the information-seeking (i.e., \"This is happening to me\") and support-seeking functions of long-haulers\' talk (i.e., \"Is this happening to you?\"). Findings have implications for collective uncertainty management, online crowdsourcing, and patient advocacy.
We recommend that health care providers employ sensitivity when addressing the anxiety that long-haulers are experiencing while also validating that their physical symptoms are real. Online communities help long-haulers manage their uncertainty.
31,892 posts on the long-haulers subreddit (r/covidlonghaulers) were analyzed, starting with its creation date, July 24th, 2020, until January 7, 2021. The Meaning Extraction Method was used to identify clusters of words that mathematically group together across the text observations.
Analyses yielded 16 distinct factors of words, which we thematized based on their composition, the data, and UMT. The 16 themes encompassed symptoms (e.g., pain, respiratory, sensory), diagnostic concerns (testing, diagnosis), broad health concerns (immunity, physical activity, diet), chronicity, support, identity, and anxiety.
Findings provide a succinct, yet robust set of themes reflecting the information-seeking (i.e., \"This is happening to me\") and support-seeking functions of long-haulers\' talk (i.e., \"Is this happening to you?\"). Findings have implications for collective uncertainty management, online crowdsourcing, and patient advocacy.
We recommend that health care providers employ sensitivity when addressing the anxiety that long-haulers are experiencing while also validating that their physical symptoms are real. Online communities help long-haulers manage their uncertainty.
摘要：
长期COVID综合征的原因和治疗方法尚不清楚。借鉴不确定性管理理论(UMT)，这项研究阐明了众包医学的传播性质，作为COVID“长途运输者”对他们知之甚少的疾病做出反应的一种手段。
分析了长途运输者subreddit（r/covidlonghaulers）上的31,892个帖子，从它的创建日期开始，7月24日,2020年，至2021年1月7日。含义提取方法用于识别在文本观察中数学分组在一起的单词簇。
分析得出了16个不同的单词因素，我们根据它们的组成进行了主题化，数据,和UMT。16个主题包含症状(例如，疼痛,呼吸,感官)，诊断问题(测试，诊断），广泛的健康问题(免疫力，身体活动,饮食)，慢性，支持,身份，和焦虑。
研究结果提供了一个简洁的，但反映信息寻求的一组强大的主题(即，\“这正在发生在我身上\”)和长途运输者的寻求支持功能（即，\“这会发生在您身上吗？”）。研究结果对集体不确定性管理有影响，在线众包，耐心的倡导。
我们建议医疗保健提供者在解决长途运输者所经历的焦虑时使用敏感性，同时也验证他们的身体症状是真实的。在线社区帮助长途运输者管理他们的不确定性。
分析了长途运输者subreddit（r/covidlonghaulers）上的31,892个帖子，从它的创建日期开始，7月24日,2020年，至2021年1月7日。含义提取方法用于识别在文本观察中数学分组在一起的单词簇。
分析得出了16个不同的单词因素，我们根据它们的组成进行了主题化，数据,和UMT。16个主题包含症状(例如，疼痛,呼吸,感官)，诊断问题(测试，诊断），广泛的健康问题(免疫力，身体活动,饮食)，慢性，支持,身份，和焦虑。
研究结果提供了一个简洁的，但反映信息寻求的一组强大的主题(即，\“这正在发生在我身上\”)和长途运输者的寻求支持功能（即，\“这会发生在您身上吗？”）。研究结果对集体不确定性管理有影响，在线众包，耐心的倡导。
我们建议医疗保健提供者在解决长途运输者所经历的焦虑时使用敏感性，同时也验证他们的身体症状是真实的。在线社区帮助长途运输者管理他们的不确定性。
