Relationship between subjective well-being and self-care among caregivers in pandemic times

Self-care refers to disease prevention and health maintenance practices. Self-care capacity can be an important factor in different conditions and contexts and when associated with aspects of an individual's health it can provide direct benefits to this person. During the Pandemic (COVID-19) this ability may be diminished or impaired, and even hindered with affective aspects, influencing their subjective well-being, that is, affecting their assessment of their own well-being. Thus, this study aimed to evaluate the relationship between self-care capacity and the affective attribution (positive or negative) of male and female adults who act as caregivers. The instruments used were a sociodemographic questionnaire, the Positive and Negative Affect Schedule and the Scale to Assess Self-Care Capabilities. After analyzing the frequency of the variables, Pearson's correlation analysis was performed and through it, moderate associations (medium high) were revealed between the measure of positive affect and self-care (r=0.62;p=0.000) and negative affect and self-care, in this case, with a negative coefficient (r=-0.42;p=0.000);thus, this indicates that people tend to have more self-care, have better positive affects and less negative affects, respectively. It was possible to infer the idea that well-being and quality of life are intertwined with the affective attribution of the individual. © 2022 Centro Universitario Sao Camilo. All rights reserved.

Predicting COVID-19 Patient Shielding: A Comprehensive Study

There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding—identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem. We use publicly available, de-identified ICU medical text data for our experiments. The labels are derived from the published COVID-19 patient shielding data. We present an extensive comparison across 12 multi-label classifiers from the simple binary relevance to neural networks and the most recent transformers. To the best of our knowledge this is the first comprehensive study, where such a range of multi-label classifiers for medical text are considered. We highlight the benefits of various approaches, and argue that, for the task at hand, both predictive accuracy and processing time are essential. © 2022, Springer Nature Switzerland AG.