Latent Dirichlet allocation topic modeling of free‐text responses exploring the negative impact of the early COVID‐19 pandemic on research in nursing

AIM: To derive latent topics from free‐text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic‐based and clinical‐based researchers. METHODS: We performed a secondary analysis of free‐text responses from a cross‐sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic‐based and clinical‐based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic‐associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. RESULTS: A total of 201 respondents (163 academic‐based and 38 clinical‐based researchers) provided free‐text responses. LDA identified eight and three latent topics for the academic‐based and clinical‐based researchers, respectively. While PCA re‐grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was “barriers to conducting research,” with the remaining themes differing between the groups. CONCLUSIONS: Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free‐text responses about the negative impact of the pandemic between academic‐based and clinical‐based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.