Peculiarities of psychological assistance in overcoming the consequences of COVID-19: a resilience approach

Introduction: The urgency of the study is due to the appearance of an emergency caused by an extremely dangerous outbreak of the COVID-19 pandemic. Every emergency, especially one which threatens health, is characterized by increased anxiety and depression among the population, causes chronic emotional disorders and requires provision of psychological and psychosocial assistance to the individual. In this regard, this article aims to describe and summarize theoretical and empirical research that will help identify the factors that shape the appropriate resource strategies for the development of resilience in overcoming the consequences of COVID-19. At the same time, this study revealed the role of resilience as a potential protective factor for mental health during an outbreak of the COVID-19 pandemic. Material and methods: The leading method of research is theoretical and methodological analysis, comprehension, comparison, classification and generalization of the main content of bibliographic sources that are relevant to the problem and purpose of the study. Results: The article presents the factors influencing resilience in the individual overcoming the consequences of COVID-19 taking into account the world experience. The main approaches to providing psychological assistance to a person in difficult life circumstances caused by the pandemic are identified. Conclusions: The factors influencing the resilience of a person during the COVID-19 pandemic are substantiated and singled out. The materials of the article are of practical value and extremely important for psychologists, psychotherapists, and physicians working to eliminate the consequences of the pandemic.

COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics

COVID-19 research datasets are crucial for analyzing research dynamics. Most collections of COVID-19 research items do not to include cited works and do not have annotations from a controlled vocabulary. Starting with ZB MED KE data on COVID-19, which comprises CORD-19, we assemble a new dataset that includes cited work and MeSH annotations for all records. Furthermore, we conduct experiments on the analysis of research dynamics, in which we investigate predicting links in a co-annotation graph created on the basis of the new dataset. Surprisingly, we find that simple heuristic methods are better at predicting future links than more sophisticated approaches such as graph neural networks. © 2021 IEEE.