Faculty of Biology, University of Cambridge, UK.
Department of Experimental Psychology, University of Oxford, UK.
Soc Sci Med. 2023 Sep;333:116130. doi: 10.1016/j.socscimed.2023.116130. Epub 2023 Jul 28.
Research has identified long COVID as the first virtual patient-made condition (Callard and Perego, 2021). It originated from Twitter users sharing their experiences using the hashtag #longcovid. Over the first two years of the pandemic, long COVID affected as many as 17 million people in Europe (WHO, 2023). This study focuses on the initial #longcovid tweets in 2020 (as previous studies have focused on 2021-2022), from the first tweet in May to August 2020, when the World Health Organization recognised the condition. We collected over 31,000 tweets containing #longcovid from Twitter. Using Braun and Clarke's reflexive thematic analysis (2020), informed by the first author's experience of long COVID and drawing on Ian Hacking's perspective on social constructionism (1999), we identified different grades of social constructionism in the tweets. The themes we generated reflected that long COVID was a multi-system, cyclical condition initially stigmatised and misunderstood. These findings align with existing literature (Ladds et al., 2020; Rushforth et al., 2021). We add to the existing literature by suggesting that Twitter users raised awareness of long COVID by providing social consensus on their long COVID symptoms. Despite the challenge for traditional evidence-based medicine to capture the varied and intermittent symptoms, the social consensus highlighted that these variations were a consistent and collective experience. This social consensus fostered a collective social movement, overcoming stigma through supportive tweets and highlighting their healthcare needs using #researchrehabrecognition. The #longcovid movement's work was revolutionary, as it showed a revolutionary grade of social constructionism, because it brought about real-world change for long COVID sufferers in terms of recognition and the potential for healthcare provisions. Twitter users' accounts expose the limitations of traditional evidence-based medicine in identifying new conditions. Future research on novel conditions should consider various research paradigms, such as Evidence-Based Medicine Plus (Greenhalgh et al., 2022).
研究将长新冠确定为首个由虚拟患者创造的病症（Callard 和 Perego，2021）。它源于推特用户使用 #longcovid 标签分享他们的经历。在大流行的头两年，欧洲有多达 1700 万人受到长新冠的影响（世界卫生组织，2023）。本研究聚焦于 2020 年长新冠的首批推特，即之前的研究集中于 2021-2022 年，时间跨度从 2020 年 5 月至 8 月，世界卫生组织在这期间确认了该病症。我们从推特上收集了超过 31000 条包含 #longcovid 的推文。采用 Braun 和 Clarke 的反思性主题分析（2020），并基于第一作者长新冠的个人经历，借鉴 Ian Hacking 的社会建构主义观点（1999），我们在这些推文中确定了不同程度的社会建构主义。我们生成的主题反映了长新冠是一种多系统、周期性的病症，最初被污名化和误解。这些发现与现有文献一致（Ladds 等人，2020；Rushforth 等人，2021）。我们通过表明推特用户通过对长新冠症状提供社会共识，提高了对长新冠的认识，从而为现有文献做出了补充。尽管传统的基于证据的医学在捕捉多变和间歇性症状方面存在挑战，但这种社会共识强调了这些变化是一种一致和集体的体验。这种社会共识促进了集体社会运动，通过支持性的推文消除污名，并使用 #researchrehabrecognition 强调他们的医疗保健需求。#longcovid 运动的工作是革命性的，因为它展示了一种革命性的社会建构主义程度，因为它为长新冠患者在认可和潜在的医疗保健方面带来了现实世界的变化。推特用户的账户暴露了传统基于证据的医学在识别新病症方面的局限性。未来对新病症的研究应考虑各种研究范式，如基于证据的医学加（Greenhalgh 等人，2022）。
