Sexual and gender minority (SGM) people are at increased risk for psychological distress compared with cisgender heterosexual people. Specific SGM subgroups include lesbian, gay, bisexual, gender diverse, and asexual people who each experience unique psychosocial challenges that can result in different mental health outcomes. The coronavirus disease 2019 (COVID-19) pandemic may have further exacerbated mental health disparities among these groups. The aim of this study was to compare lesbian, gay, bisexual, gender diverse, asexual, and cisgender heterosexual people's mental health and social support during the first 4 months of the COVID-19 crisis. This study used a cross-sectional online survey from March 26th, 2020 to July 7th, 2020 in Québec, Canada. A total of 2908 individuals ( = 304 SGM people, = 2604 cisgender heterosexual people) completed questionnaires measuring perceived social support, perceived stress, symptoms of depression and anxiety, as well as loneliness. SGM people presented worse health outcomes than cisgender heterosexual people on all questionnaires ( < 0.001). analyses showed that particularly marginalized SGM subgroups, including bisexual and asexual people, reported the poorest mental health. Moderation analyses revealed that the buffering effect of social support on depressive symptoms was four times stronger among SGM people (ΔR = 0.041; < 0.001) than among cisgender heterosexual people (ΔR = 0.010; < 0.001). This study suggests that fostering social connectedness among SGM people may be especially beneficial in buffering against distress in the face of a crisis.
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http://dx.doi.org/10.1089/lgbt.2021.0255 | DOI Listing |
The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.
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May 2020
Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.
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November 2024
Department of Toxicology, Drug Industry, Management and Legislation, Faculty of Pharmacy, "Victor Babeş" University of Medicine and Pharmacy, 2nd Eftimie Murgu Sq., 300041 Timişoara, Romania.
The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
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December 2024
Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.
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