Patterns of exposure to intimate partner violence (IPV) and child abuse (CA) were explored in 467 women seeking psychological assistance following IPV. Using latent class analysis, three classes were obtained: women who had experienced physical, sexual, and psychological IPV, along with childhood physical and sexual abuse (IPV + CA; 38.5%); women who had experienced physical, sexual, and psychological IPV only (IPV/no CA; 52.9%); and women who had experienced psychological IPV only (Psych IPV only; 8.6%). Associations of class membership with severity of specific mental health conditions were examined, along with the number of diagnosed conditions. Significant between-class differences were noted on severity of IPV-related posttraumatic stress disorder, depressive disorders, alcohol and substance use disorders, and social phobia. Classes also differed significantly on the number of mental health conditions. Understanding patterns of betrayal-based trauma (e.g., IPV and CA) can inform care within agencies that serve IPV survivors by highlighting individuals at-risk for mental health conditions.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/10775595211031655 | 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.
View Article and Find Full Text PDFInt Psychogeriatr
May 2020
Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.
Viruses
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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!