Dataset Information

Mental Health During the COVID-19 Pandemic in Japan: Applying Topic Modeling in Daily Life Descriptions.

ABSTRACT: The novel coronavirus disease pandemic is threatening not only physical but also mental health. Although some recent quantitative studies have been conducted and revealed the influence of the pandemic on mental health and its relevant factors, it is impossible to obtain and explore all possible variables strongly related to mental health. Therefore, we attempted to adopt a bottom-up approach using text mining of participants' narratives. We examined how participants' descriptions of daily life during the pandemic were categorized into various topics, and which topics were related to their mental health in a sample of 776 Japanese citizens in the general population over 18 years old. Results of a topic modeling with 2,594 unique words provided nine topics (mask, physical symptoms, children, infection anxiety, disinfection items, economic influence, remote work, going out, and change of lifestyle). Those who wrote about economic influence, physical symptoms, and disinfection items experienced lower life satisfaction and higher depression and negative affect, whereas those who mentioned their children were likely to have higher life satisfaction. This study highlighted that monitoring the mental health of individuals with economic impacts and physical symptoms may reduce the damage of COVID-19.

Supplementary information

The online version contains supplementary material available at 10.1007/s11469-021-00587-y.

SUBMITTER: Chishima Y

PROVIDER: S-EPMC8628833 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Mental Health During the COVID-19 Pandemic in Japan: Applying Topic Modeling in Daily Life Descriptions.

Chishima Yuta Y Liu I-Ting Huai-Ching IH

International journal of mental health and addiction 20211129 1

The novel coronavirus disease pandemic is threatening not only physical but also mental health. Although some recent quantitative studies have been conducted and revealed the influence of the pandemic on mental health and its relevant factors, it is impossible to obtain and explore all possible variables strongly related to mental health. Therefore, we attempted to adopt a bottom-up approach using text mining of participants' narratives. We examined how participants' descriptions of daily life d ...[more]

PMID: 34867123

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Project description:BackgroundThe COVID-19 pandemic has spread at an alarming speed, and effective treatment for the disease is still lacking. The body of evidence on COVID-19 has been increasing at an impressive pace, creating the need for a method to rapidly assess the current knowledge and identify key information. Gold standard methods such as systematic reviews and meta-analyses are regarded unsuitable because they have a narrow scope and are very time consuming.ObjectiveThis study aimed to explore the published scientific literature on COVID-19 and map the research evolution during the early phase of the COVID-19 pandemic.MethodsWe performed a PubMed search to analyze the titles, keywords, and abstracts of published papers on COVID-19. We used latent Dirichlet allocation modeling to extract topics and conducted a trend analysis to understand the temporal changes in research for each topic, journal impact factor (JIF), and geographic origin.ResultsBased on our search, we identified 16,670 relevant articles dated between February 14, 2020, and June 1, 2020. Of these, 6 articles were reports from peer-reviewed randomized trials on patients with COVID-19. We identified 14 main research topics, of which the most common topics were health care responses (2812/16,670, 16.86%) and clinical manifestations (1828/16,670, 10.91%). We found an increasing trend for research on clinical manifestations and protective measures and a decreasing trend for research on disease transmission, epidemiology, health care response, and radiology. Publications on protective measures, immunology, and clinical manifestations were associated with the highest JIF. The overall median JIF was 3.7 (IQR 2.6-5.9), and we found that the JIF for these publications declined over time. The top countries producing research were the United States, China, Italy, and the United Kingdom.ConclusionsIn less than 6 months since the novel coronavirus was first detected, a remarkably high number of research articles on COVID-19 have been published. Here, we discuss and present the temporal changes in the available COVID-19 research during the early phase of the pandemic. Our findings may aid researchers and policy makers to form a structured view of the current COVID-19 evidence base and provide further research directions.

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Dataset Information

Mental Health During the COVID-19 Pandemic in Japan: Applying Topic Modeling in Daily Life Descriptions.

Supplementary information

Publications

Mental Health During the COVID-19 Pandemic in Japan: Applying Topic Modeling in Daily Life Descriptions.

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