The significant rise in the prevalence of mental health disorders among school children and adolescents in Lebanon and conflict zones necessitate immediate interventions. Despite this elevated prevalence, such communities currently lack effective programs which clearly identify the concepts of mental health promotion and prevention among school children. Addressing this gap, our paper aims to propose a contextual model and framework for educators, researchers and policy-makers. The proposed model includes 3-levels comprising set of actions and interventions. Interventions adopted at each level should be fine-tuned to produce a caring and supportive learning environment that effectively incorporates the three essential domains of student's mental health: promoting mental health protective factors, notably social and emotional learning (SEL) and resilience, as well as preventing mental health disorders (MHD). Interventions set at each level are elaborated in the framework and linked to the corresponding segments present in the school environment to include teachers, family, community, media, and policymakers. The framework also extends these interventions to encompass the core competencies of SEL, resilience, as well as the prevention of the most prevalent mental health disorders in conflict zones.
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http://dx.doi.org/10.1007/s10597-024-01322-2 | 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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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.
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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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December 2024
Abu Dhabi Maritime Academy, Abu Dhabi P.O. Box 54477, United Arab Emirates.
Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly.
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