Repercussions of infodemia associated with COVID-19 on the mental health of the elderly in Brazil

The phenomenon called infodemia refers to the increase in the volume of information on a specific topic, which multiplies rapidly in a short period of time, and has stood out in the context of the health crisis triggered by the COVID-19 pandemic. Too much information can trigger feelings of fear, anxiety, stress, and other conditions of mental distress. The study aims to describe the profile of exposure to information about COVID-19 and its repercussions on the mental health of elderly Brazilians. This is a cross-sectional study carried out with 1924 elderly Brazilians. Data were collected through a web-based survey sent to the elderly via social networks and email, from July to October 2020. The results of the descriptive analysis of the data show that most of the elderly were aged between 60 and 69 years (69.02%), female (71.26%), married (53.79%) and white (75.57%). About 21.67% (n = 417) concluded their graduation, 19.75% (380) concluded their specialization and 16.63% (320) concluded their master's or doctoral degrees. Television 862 (44.80%) and social networks 651 (33.84%) were reported as frequent sources of exposure to news or information about COVID-19. Participants indicated that television (46.47%;n = 872), social networks (30.81%;n = 575) and radio (14.48%;251) affected them psychologically and/or physically. Receiving fake news about COVID-19 on television (n = 482;19.8%) and on social media (n = 415;21.5%) mainly resulted in stress and fear. The disseminated information contributes to awareness, but also affects physically and/or psychologically many elderly people, mainly generating fear and stress. © 2022, Centro Nacional de Informacion de Ciencias Medicas. All rights reserved.

A Fuzzy Theory Based Topological Distance Measurement for Undirected Multigraphs

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

A fuzzy theory based topological distance measurement for undirected multigraphs

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods. © 2020 IEEE.

Nursing process directed to patients with COVID-19: record of the stages

Objective: to identify the record of the stages of the Nursing Process directed to patients with COVID-19. Method: descriptive and documentary research, with analysis of 37 medical records. Results: 83.8% of the medical records presented a record of Nursing Data Collection;56.8%, from the Nursing Assessment;and, 51.4%, of Implementation. However, no records were identified involving the stage of Nursing Diagnosis and Nursing Planning. Conclusion: the registration has occurred in an incipient and discontinuous way;however, it is an analysis carried out in a pandemic scenario, in which the professional's overload and feelings of helplessness and insecurity must be considered. Therefore, it is suggested that research be carried out to assess the impact of the pandemic in the context of nursing, thus enabling subsidies for the development of strategies that aim to support the registration of the Nursing Process by the professional.