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

Nursing researchers' concern about research activities during the COVID-19 pandemic: A secondary analysis of longitudinal survey data in Japan

The coronavirus disease (COVID-19) pandemic has negatively affected research activities across various fields. This study aimed to determine nursing researchers' concerns about research activities during the COVID-19 pandemic in Japan and subsequent changes brought on by it. For this study, we conducted descriptive statistics and text mining analyses using data from two surveys conducted by the Japan Academy of Nursing Science (JANS) in the early days of the pandemic (first survey: mid-2020) and after 2 years (second survey: early 2022). Concerns about research activities were observed in 89% and 80% of the nursing researchers in the first and second surveys, respectively. Furthermore, concerns about "Difficulty in collecting research data" and "Content and quality of your research" were stronger in the second survey. Text mining analyses revealed that in the first survey, they were concerned about environmental changes and restrictions when proceeding with research during the COVID-19 pandemic, which was unfamiliar at the time. In the second survey, after overcoming environmental changes in the early stages of the pandemic, nursing researchers' concerns shifted to anxiety about the future, such as concerns about degree acquisition, employment and career advancement, and research results. The current study highlights various concerns among nursing researchers regarding research activities that have evolved over time during the pandemic. Academic societies must flexibly construct support measures for nursing researchers when a new infectious disease occurs. Such measures should be sensitive to the prevailing social circumstances and the evolving needs of researchers.

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

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.

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.