Analysis of Patterns and Trends in COVID-19 Research

Dornick, Christopher; Kumar, Amit; Seidenberger, Scott; Seidle, Elizabeth; Mukherjee, Partha

doi:10.1016/j.procs.2021.05.032

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…These results showed a significant publication trend during the COVID-19 pandemic. 39,40 The COVID-19 pandemic has created many ethical challenges that require HCWs to find the “right way or best method” to manage it. 41,42 Several studies have revealed differences in the trend of ethical issues related to willingness to work during disasters.…”

Section: Discussionmentioning

confidence: 99%

Trends and ethical issues in nursing during disasters: A systematic review

2023

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Background During a disaster, nurses face complex ethical challenges because of risky situations. It is necessary to identify trends and ethical issues of nurses in disasters to improve the quality of care and impact for nurses. Method This systematic review enrolled in the international registration with PROSPERO: CRD42022350765. We searched the following databases: PubMed, EBSCO MEDLINE, SCOPUS, ProQuest, ScienceDirect, and Sage Pub. The inclusion criteria were developed according to PICO and D; are Population (F): involving nurses; intervention/Exposure (I): disaster, Comparison (C): none; outcome (O): ethical practice. And Design (D): qualitative, quantitative, and mixed methods. Years of publication were 2012–2022, with full text in English. The quality of study assessment used was The Joanna Briggs Institute (JBI) Critical Appraisal tool and Mixed Methods Appraisal Tool (MMAT) version 2018. Analysis used PICO synthesis. Results There were 15 studies reviewed from 2093 results, including cross-sectional (4/15, 26.6%), qualitative (9/15, 60%), and mixed methods (2/15, 13.3%) studies. The types of disasters were: COVID-19 (7/15, 46.6%), infectious (4/15, 26.6%), and all disaster events (4/15, 26.6%). The main themes were: (1) ethical issues in disasters have the potential to address ethical dilemmas, (2) factors applying nursing ethics to support ethical decision-making in disasters, (3) strategies for applying ethics and dealing with ethical issues in disasters, and (4) the impact of applying ethics in disasters. Conclusion Applying ethics nursing in a disaster is influenced by various factors. This framework for ethical nursing in disasters aims to help nurses, educational institutions, and policymakers develop schemes or scenarios to enhance responsible ethical decisions in disasters.

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Section: Discussionmentioning

confidence: 99%

Trends and ethical issues in nursing during disasters: A systematic review

2023

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“…Named Entity Recognition (NER) can effectively extract information on named entities, such as people, ages, and locations, as well as clinical or medical entities including drugs [ 45 , 46 ], from the text data. Topic modelling, which is a widely used NLP method, can uncover hidden patterns in the data, such as non-medical use of prescription medicines and related issues [ 47 , 48 ]. The novelty of our approach lies in integrating different NLP modules in a pipeline structure.…”

Section: Methodsmentioning

confidence: 99%

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

et al. 2023

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Background Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications. Methods We collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication. Results The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities. Conclusions NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

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Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study

Ye,

Wang,

Lin

2024

J Epidemiol Glob Health

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Background The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated through traditional systematic review. Our study aims to systematically synthesize the evidence using natural language processing (NLP) techniques. Methods Multiple databases were searched using titles, abstracts, and keywords. We systematically identified relevant literature published prior to Dec 31, 2023, using NLP techniques such as text classification, topic modelling and geoparsing methods. Relevant articles were categorized by content, date, and geographic location, outputting evidence heat maps, geographical maps, and narrative synthesis of trends in related publications. Results Our NLP analysis identified 77,915 studies in the area of pandemics or epidemics and mental health published before Dec 31, 2023. The Covid pandemic was the most common, followed by SARS and HIV/AIDS; Anxiety and stress were the most frequently studied mental health outcomes; Social support and healthcare were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries, with scant evidence from low-income counties. Co-occurrence of pandemics or epidemics and fear, depression, stress was common. Anxiety was one of the three most common topics in all continents except North America. Conclusion Our findings suggest the importance and feasibility of using NLP to comprehensively map pandemics or epidemics and mental health in the age of big literature. The review identifies clear themes for future clinical and public health research, and is critical for designing evidence-based approaches to reduce the negative mental health impacts of pandemics or epidemics. Supplementary Information The online version contains supplementary material available at 10.1007/s44197-024-00284-8.

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Analysis of Patterns and Trends in COVID-19 Research

Cited by 5 publications

References 12 publications

Trends and ethical issues in nursing during disasters: A systematic review

Trends and ethical issues in nursing during disasters: A systematic review

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study

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