Little is known about the factors affecting the recovery of mental health in COVID-19 patients. The purpose of this study is to look into the change of psychological distress and to explore the role of negative appraisals in the improvement of psychological distress in COVID-19 patients after they recovered from the infection. We conducted a longitudinal survey on patients with COVID-19 infection in Changsha. The 9-item Patient Health scale, the 7-item Generalized Anxiety Disorder scale, and a newly developed measure, the COVID-19 Impact Scale (CIS) were applied to assess patients' depression, anxiety, and negative appraisal toward COVID-19 infection during their hospitalization and 1 month post-discharge. Seventy-two patients were included in the analysis. A significant decrease in anxiety and depression levels was observed after patients were discharged from hospital. Two meaningful factors of the CIS were extracted based on factor analysis, namely "health impact," and "social impact." The change of social impact explained the 12.7 and 10.5% variance in the depression and anxiety symptom improvement, respectively. Change in negative appraisals, especially the appraisals related to COVID-19 social impact may play a vital role in the relief of psychological distress of infected patients. Therefore, a cognitive and social care perspective might be considered when promoting the mental health recovery and readjustment to society among COVID-19 patients.
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http://dx.doi.org/10.3389/fpsyt.2021.585537 | DOI Listing |
Ann N Y Acad Sci
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Individuals with high math anxiety (HMA) demonstrate a tendency to avoid math-related tasks, a behavior that perpetuates a detrimental cycle of limited practice, poor performance, increased anxiety, and further avoidance. This study delves into the cognitive and neural bases of math avoidance behavior in HMA through the lens of reward processing. In Experiment 1, participants reported their satisfaction level in response to the reward provided after solving an arithmetic problem.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
The aim of the study was to investigate the relationship between basic psychological needs satisfaction, coping functions, cognitive appraisals, emotions, and psychobiosocial experiences in competitive athletes. Multi-states (MuSt) theory was used as the theoretical framework. The study involved a convenience sample of 183 Italian athletes (102 men), aged 16 to 48 years (M = 24.
View Article and Find Full Text PDFActas Esp Psiquiatr
January 2025
Centro Universitário Investigação em Psicologia (CUIP) Universidade do Algarve, 8005-139 Faro, Portugal; Departamento de Psicologia e Ciências da Educação, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 8005-139 Faro, Portugal.
Background: Mental contamination (MC) refers to feelings of internal filthiness associated with contamination obsessions. Ego-dystonic memories and thoughts can trigger MC, although it can also be activated by trauma, which is associated with the onset of post-traumatic stress disorder (PTSD). Research shows that MC, negative emotions and PTSD can occur simultaneously.
View Article and Find Full Text PDFNutr Rev
January 2025
Department of Public Health, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia.
Context: Inconsistent results have been reported regarding the prevalence of and factors associated with formula feeding in Ethiopia.
Objective: This study aimed to determine the pooled prevalence of and factors associated with formula feeding among mothers with infants 0-6 months of age in Ethiopia.
Data Sources: A comprehensive systematic search was conducted across 3 databases (PubMed, EMBASE, and ScienceDirect) and the Google Scholar search engine to identify relevant studies published up to April 2, 2024.
Jpn J Radiol
January 2025
Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer detection (CAD) and risk prediction. While AI CAD systems are slowly finding its way into clinical practice to assist radiologists or make independent reads, this review focuses on AI risk models, which aim to predict a patient's likelihood of being diagnosed with breast cancer within a few years after negative screening. Unlike AI CAD systems, AI risk models are mainly explored in research settings without widespread clinical adoption.
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