Prediction models for the impact of the COVID‐19 pandemic on research activities of Japanese nursing researchers using deep learning

Abstract

Aim This study aimed to construct and evaluate prediction models using deep learning to explore the impact of attributes and lifestyle factors on research activities of nursing researchers during the COVID‐19 pandemic. Methods A secondary data analysis was conducted from a cross‐sectional online survey by the Japanese Society of Nursing Science at the inception of the COVID‐19 pandemic. A total of 1089 respondents from nursing faculties were divided into a training dataset and a test dataset. We constructed two prediction models with the training dataset using artificial intelligence (AI) predictive analysis tools; motivation and time were used as predictor items for negative impact on research activities. Predictive factors were attributes, lifestyle, and predictor items for each other. The models' accuracy and internal validity were evaluated using an ordinal logistic regression analysis to assess goodness‐of‐fit; the test dataset was used to assess external validity. Predicted contributions by each factor were also calculated. Results The models' accuracy and goodness‐of‐fit were good. The prediction contribution analysis showed that no increase in research motivation and lack of increase in research time strongly influenced each other. Other factors that negatively influenced research motivation and research time were residing outside the special alert area and lecturer position and living with partner/spouse and associate professor position, respectively. Conclusions Deep learning is a research method enabling early prediction of unexpected events, suggesting new applicability in nursing science. To continue research activities during the COVID‐19 pandemic and future contingencies, the research environment needs to be improved, workload corrected by position, and considered in terms of work‐life balance.

“The impact of the COVID-19 pandemic on research activities: A survey of the largest Italian academic community”

“…Furthermore, female researchers are more likely to shoulder domestic duties following the closure of universities and research institutions, as well as childbearing and home-schooling when lockdowns also involved schools and kindergartens [39]. Indeed, it has been postulated that many women are currently doing 'second and third shifts' regarding housework and welfare after their first shift of paid work [40][41][42][43]. The possible explanation of our data relies on the lack of information on family composition and the presence of offspring from participants in our survey, as parents are expected to suffer more significantly from the issues mentioned above [3].…”