Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution

Liu, Jiaying; Nie, Hansong; Li, Shihao; Chen, Xiangtai; Cao, Huazhu; Ren, Jing; Lee, Ivan; Xia, Feng

doi:10.1016/j.bdr.2021.100236

Cited by 24 publications

(15 citation statements)

References 25 publications

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“…Due to the complexity and variability of pandemic outbreaks, it is significant to systematically and dynamically identify research priorities in each key stage of the pandemic development process, especially in the early stages of a pandemic. Since the outbreak of the pandemic such as COVID-19, there has been a surge in published COVID-19 research literature in the early stage [ 20 ], which provided scientific materials for the identification of emergency research priorities and can be considered as the key proxy of reflecting the trend of research in both theory and practice [ 21 ]. With the more and more extensive application of text analysis based on machine learning, it is possible to quickly analyze a large number of literatures [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management

Luo

Chai

Pan

2021

IJERPH

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Novel coronavirus disease 2019 (COVID-19) pandemic has had a great impact on global production and life in the past period. Countless researchers devoted themselves to rescuing patients and reducing its impact. Analyzing the literature published during the pandemic and identifying the research priorities is of great significance to quickly discover research gaps, rationally allocate scientific research resources, and promote the development of the global research platform. To understand the swing of research priorities during the pandemic, this paper proposed a research priorities identification framework for pandemic based on scientific literature text analysis. Moreover, a research priority metric model was proposed to measure the characteristics of research priorities, and the empirical analysis from COVID-19 scientific literature was conducted to identify the research priorities during the pandemic. As a result, the research priorities identified by the method proposed in this paper discovered the fine-grained dynamic characteristics along with the process in the pandemic outbreak, and based on this, the emergency scientific research response strategies were discussed to give implications for the public health emergency scientific research and management.

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Section: Introductionmentioning

confidence: 99%

Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management

Luo

Chai

Pan

2021

IJERPH

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“…This review has a number of limitations. Notably, the pace of COVID-19 research is extremely rapid (Liu et al, 2021b ). This review is limited to trials registered by 31 May 2022; any trials registered after this date, or not registered, are not included.…”

Section: Discussionmentioning

confidence: 99%

Interventions for mental health, cognition, and psychological wellbeing in long COVID: a systematic review of registered trials

et al. 2022

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Background. Among patients diagnosed with COVID-19, a substantial proportion are experiencing ongoing symptoms for months after infection, known as "long COVID". Long COVID is associated with a wide range of physical and neuropsychological symptoms, including impacts on mental health, cognition, and psychological wellbeing. However, intervention research is only beginning to emerge. This systematic review synthesizes currently registered trials examining interventions for mental health, cognition, and psychological wellbeing in patients with long COVID. Methods. Standard systematic review guidelines were followed. Trials registered in two large trial registries in 2020 to May 2022 were reviewed. Included studies were narratively synthesized by type of intervention and a risk-of-bias assessment was conducted. Results. 42 registered trials were included, with a total target sample size of 5814 participants. These include 11 psychological interventions, five pharmacological and other medical interventions, and five evaluating herbal, nutritional, or natural supplement interventions. An additional nine trials are examining cognitive and neurorehabilitation interventions and twelve are examining physiotherapy or physical rehabilitation. Most trials are randomized, but many are feasibility trials; trials are evaluating a wide spectrum of outcomes. Conclusions. While there is a newly emerging body of research testing interventions for mental health, cognition, and psychological wellbeing in long COVID, the breadth and scope of the research remains limited. Building upon the trials currently under way, it is urgently incumbent on researchers to expand upon the intervention research currently under way, in order to generate high-quality evidence on a wide range of candidate interventions for diverse long COVID patient populations.

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“…Meanwhile, the epidemic of COVID-19 has led to teaching being undertaken remotely and on digital platforms, which has extensively promoted the growth of online education. This process generates a lot of data for online education (Liu et al, 2021;Yu et al, 2021b). These changes have contributed significantly to the development of big data technologies in education and provide a unique opportunity for educational anomaly analytics (Hou et al, 2019;Al-Doulat et al, 2020;AlKhuzaey et al, 2021;Ren et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution

Cited by 24 publications

References 25 publications

Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management

Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management

Interventions for mental health, cognition, and psychological wellbeing in long COVID: a systematic review of registered trials

Educational Anomaly Analytics: Features, Methods, and Challenges

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