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Female versus viral: Understanding the UK gender health inequalities during the Covid-19 pandemic using e-archives

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  • Qu, Chen

Abstract

Despite the development of digital health infrastructure, female health inequalities have worsened during the pandemic. This transdisciplinary study, through health, feminist, and infrastructural geographical lens, examines how gender health inequalities may have emerged or worsened during Covid-19 in the UK. This study leverages a novel web archive collection, Python coding-powered data-handling text analysis (of over 0.2 billion words), and thematic analysis to examine three themes: vaccines, social minority groups, and women’s self-care. The findings suggest that the pandemic has impacted health inequalities among British women and girls and more, in a ‘more-than-gender’ way in terms of health (care) outcomes and access. In addition to reflecting on the use of e-archives in this study including suggesting the potential of combining e-archiving, coding, natural language processing (NLP) and generative AI/Large Language Models (LLMs) in producing and analysing trans-temporal (big) datasets, I argue that a geographical crisis perspective that balances the needs of everyday life and possible crises can be considered when preparing for public health emergencies. I adopt the e-archiving of this study to rethink ‘digital health infrastructure’ as ‘actors’, ‘facilitators’, and ‘voicers’, revealing how human-computer interaction and people in the virtual realm can be infrastructure.

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  • Qu, Chen, 2025. "Female versus viral: Understanding the UK gender health inequalities during the Covid-19 pandemic using e-archives," Social Science & Medicine, Elsevier, vol. 366(C).
    • Handle: RePEc:eee:socmed:v:366:y:2025:i:c:s0277953624010438
    DOI: 10.1016/j.socscimed.2024.117589
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