The coronavirus disease (COVID-19) pandemic has substantially impacted psychological health in the U.S and has disproportionately impacted underresourced individuals. Despite the higher need for mental health services during this time, service availability and access were disrupted due to increased demand, social distancing recommendations, and stay-at-home orders. Thus, it is crucial to understand factors that predict the desire for psychological services for underresourced individuals. The present study examined factors at multiple levels of Bronfenbrenner's socioecological model (Bronfenbrenner, 1994) to determine which factors best predicted the desire for mental health services including individual, group, in-person, and online services. The sample consisted of 155 underresourced adults in North Carolina. Participants completed an online survey of mental health symptoms, coping strategies, COVID-19 related stressors, and provided demographic information including ZIP code, which was used to classify urban-central and urban-outlying dwellers. Results from univariate general linear models demonstrated that depression symptoms, venting as a coping strategy, COVID-related stress, and living in more rural regions were all significant predictors of the desire for psychological services. Venting as a predictor of the desire for services may signify a general misunderstanding regarding the purpose of psychotherapy as well as the need for individuals to gain social support and connectedness during a pandemic. This study helps to clarify individual-level and contextual factors that impact the desire for psychological services during a global pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Neuromodulation
January 2025
Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Objectives: Biphasic sinusoidal repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation treatment that has been approved by the US Food and Drug Administration for treatment-resistant depression (TRD). Recent advances suggest that standard rTMS may be improved by altering the pulse shape; however, there is a paucity of research investigating pulse shape, owing primarily to the technologic limitations of currently available devices. This pilot study examined the feasibility, tolerability, and preliminary efficacy of biphasic and monophasic rectangular rTMS for TRD.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFViruses
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.
View Article and Find Full Text PDFSensors (Basel)
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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