There is a paucity of research exploring Indigenous women's experiences in acute mental health inpatient services in Australia. Even less is known of Indigenous women's experience of seclusion events, as published data are rarely disaggregated by both indigeneity and gender. This research used secondary analysis of pre-existing datasets to identify any quantifiable difference in recorded experience between Indigenous and non-Indigenous women, and between Indigenous women and Indigenous men in an acute mental health inpatient unit. Standard separation data of age, length of stay, legal status, and discharge diagnosis were analysed, as were seclusion register data of age, seclusion grounds, and number of seclusion events. Descriptive statistics were used to summarize the data, and where warranted, inferential statistical methods used SPSS software to apply analysis of variance/multivariate analysis of variance testing. The results showed evidence that secondary analysis of existing datasets can provide a rich source of information to describe the experience of target groups, and to guide service planning and delivery of individualized, culturally-secure mental health care at a local level. The results are discussed, service and policy development implications are explored, and suggestions for further research are offered.
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http://dx.doi.org/10.1111/inm.12289 | 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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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.
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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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