Background: Diagnoses are controversial but ubiquitous in mental health; however, whether they are essential features of service entry has not been analysed.
Aim: To investigate the use of diagnosis in the service entry criteria of UK NHS adult mental health services.
Methods: Freedom of Information requests were made to 17 NHS adult mental health Trusts; responses were analysed thematically.
Results: Four service types were identified: broadly diagnostic, problem-specific, supporting specific life circumstances and needs-led. Diagnoses were used frequently but not universally. Non-diagnostic factors were central to service entry criteria.
Conclusions: Diagnoses were neither necessary nor sufficient in-service entry criteria. Broad clusters of difficulties were used rather than specific diagnoses. Extensive exceptions revealed diagnoses as inefficient proxies for risk, severity and need. Differences across criteria appeared largely driven by professional competencies. Implications for innovative care pathways include preventative services and working with psychosocial factors.
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http://dx.doi.org/10.1080/09638237.2019.1677875 | DOI Listing |
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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!