Background: Previous research suggests that many people receiving mental health treatment do not meet criteria for a mental disorder but are rather 'the worried well'.
Aims: To examine the association of past-year mental health treatment with DSM-IV disorders.
Method: The World Health Organization's World Mental Health (WMH) Surveys interviewed community samples of adults in 23 countries (n = 62 305) about DSM-IV disorders and treatment in the past 12 months for problems with emotions, alcohol or drugs.
Results: Roughly half (52%) of people who received treatment met criteria for a past-year DSM-IV disorder, an additional 18% for a lifetime disorder and an additional 13% for other indicators of need (multiple subthreshold disorders, recent stressors or suicidal behaviours). Dose-response associations were found between number of indicators of need and treatment.
Conclusions: The vast majority of treatment in the WMH countries goes to patients with mental disorders or other problems expected to benefit from treatment.
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http://dx.doi.org/10.1192/bjp.bp.113.141424 | 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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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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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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