Background: Quantitatively derived dimensional models of psychopathology enjoy overwhelming empirical support, and a large and active community of psychopathology researchers has been establishing an empirically based dimensional hierarchical taxonomy of psychopathology (or HiTOP) as a strong candidate replacement for the current categorical classification system. The hierarchical nature of this taxonomy implies that different levels of resolution are likely to be optimal for different purposes. Our aim was to identify which level of detail is likely to provide optimal validity and explanatory power with regard to relevant clinical variables.
Methods: In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services project, we used data from a sample of 2900 psychiatric outpatients to compare different levels from a bass-ackwards model of psychopathology in relation to psychosocial impairment across different domains (global functioning, inability to work, social functioning, suicidal ideation, history of suicide attempts, history of psychiatric hospitalization).
Results: All functioning indices were significantly associated with general psychopathology, but more complex levels provided significant incremental validity. The optimal level of complexity varied across functioning indices, suggesting that there is no single 'best' level for understanding relations between psychopathology and functioning.
Conclusions: Results support the hierarchical organization of psychopathology dimensions with regard to validity considerations and downstream implications for applied assessment. It would be fruitful to develop and implement measurement of these dimensions at the appropriate level for the purpose at hand. These findings can be used to guide HiTOP-consistent assessment in other research and clinical settings.
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http://dx.doi.org/10.1017/S0033291722003324 | DOI Listing |
CAZymes ( C arbohydrate A ctive En Zymes ) degrade, synthesize, and modify all complex carbohydrates on Earth. CAZymes are extremely important to research in human health, nutrition, gut microbiome, bioenergy, plant disease, and global carbon recycling. Current CAZyme annotation tools are all based on sequence similarity.
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February 2025
University of Houston, Houston, Texas, USA.
More work is needed to establish the validity of the Alternative Model of Personality Disorders (AMPD) in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Acceptance of the AMPD as the primary model of personality disorder requires identifying neurocognitive validators of AMPD-defined personality functioning and demonstrating superiority of the AMPD over the traditional categorical model of personality disorder. It is also important to establish the utility of the AMPD in a developmental context given evidence that personality disorder emerges in adolescence.
View Article and Find Full Text PDFAssist Technol
January 2025
Shaanxi Key Laboratory of Behavior and Cognitive Neuroscience, School of Psychology, Shaanxi Normal University, Xi'an, China.
Socially assistive robots (SARs) are increasingly recognized for their potential in helping older adults age in place. Effectively meeting the diverse needs of older adults requires a proper classification of SARs' functions. However, existing function categories are primarily proposed from the perspective of researchers, rarely from older adults themselves.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability.
Heliyon
December 2024
Xinxiang Medical University, Xinxiang, 453000, China.
This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness.
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