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.

Propensity-matched comparisons of factors negatively affecting research activities during the COVID-19 pandemic between nursing researchers working in academic and clinical settings in Japan.

AIM: To determine the factors associated with reduced research activities during the COVID-19 pandemic in 2020 by comparing nursing researchers working in academic and clinical settings. METHODS: This was a secondary analysis of data collected by the Japan Academy of Nursing Sciences, which conducted a cross-sectional online survey when the pandemic began. We included respondents who worked in either academic or clinical settings and responded that the pandemic negatively affected their research activities. First, we computed a propensity score (PS) using a logistic regression model. Then we performed a one-to-one ratio matching between the groups based on the PS to control imbalances between the groups. We identified the factors negatively affecting research activities and who to consult about research concerns by comparing the groups using Chi-square or Fisher's exact tests. RESULTS: There were 1,532 participants, with a response rate of 16.1%. After PS matching, 214 participants (107 for each group) were included. We identified three significant factors associated with reduced research activities: (i) time required for learning new information and communication technology (ICT) skills; (ii) time required for supporting colleagues with ICT issues; and (iii) time required for preparing and evaluating teaching materials. Approximately 20% of our participants in both settings had nobody to consult regarding research concerns. CONCLUSION: We found that the time spent on ICT-related issues negatively affected the research activities of nursing researchers when the pandemic began in Japan. In such an emergency, nursing researchers needed an opportunity to share their difficulties as a part of a support service.