Capturing the experiences of UK healthcare workers during the COVID-19 pandemic: A structural topic modelling analysis of 7,412 free-text survey responses - Lamb, Danielle; Wright, Liam; Scott, Hannah; Croak, Bethany; Gnanapragasam, Sam; Docherty, Mary; Greenberg, Neil; Hotopf, Matthew; Stevelink, Sharon A. M.; Raine, Rosalind; Wessely, Simon (PloS one, 2022)
Healthcare workers (HCWs) have provided vital services during the COVID-19 pandemic, but existing research consists of quantitative surveys (lacking in depth or context) or qualitative interviews (with limited generalisability). Structural Topic Modelling (STM) of large-scale free-text survey data offers a way of capturing the perspectives of a wide range of HCWs in their own words about their experiences of the pandemic. In an online survey distributed to all staff at 18 geographically dispersed NHS Trusts, we asked respondents, "Is there anything else you think we should know about your experiences of the COVID-19 pandemic?". We used STM on 7,412 responses to identify topics, and thematic analysis on the resultant topics and text excerpts. We identified 33 topics, grouped into two domains, each containing four themes. Our findings emphasise: the deleterious effect of increased workloads, lack of PPE, inconsistent advice/guidance, and lack of autonomy; differing experiences of home working as negative/positive; and the benefits of supportive leadership and peers in ameliorating challenges. Themes varied by demographics and time: discussion of home working decreasing over time, while discussion of workplace challenges increased. Discussion of mental health was lowest between September-November 2020, between the first and second waves of COVID-19 in the UK. Our findings represent the most salient experiences of HCWs through the pandemic. STM enabled statistical examination of how the qualitative themes raised differed according to participant characteristics. This relatively underutilised methodology in healthcare research can provide more nuanced, yet generalisable, evidence than that available via surveys or small interview studies, and should be used in future research.