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The present study compared the primary models used in research on the structure of psychopathology (i.e., correlated factor, higher-order, and bifactor models) in terms of structural validity (model fit and factor reliability), longitudinal measurement invariance, concurrent and prospective predictive validity in relation to important outcomes, and longitudinal consistency in individuals' factor score profiles. Two simpler operationalizations of a general factor of psychopathology were also examined-a single-factor model and a count of diagnoses. Models were estimated based on structured clinical interview diagnoses in two longitudinal waves of nationally representative data from the United States ( = 43,093 and = 34,653). Models that included narrower factors (fear, distress, and externalizing) were needed to capture the observed multidimensionality of the data. In the correlated factor and higher-order models these narrower factors were reliable, largely invariant over time, had consistent associations with indicators of adaptive functioning, and had moderate stability within individuals over time. By contrast, the fear- and distress-specific factors in the bifactor model did not show good reliability or validity throughout the analyses. Notably, the general factor of psychopathology ( factor) performed similarly well across tests of reliability and validity regardless of whether the higher-order or bifactor model was used; the simplest (single factor) model was also comparable across most tests, with the exception of model fit. Given the limitations of categorical diagnoses, it will be important to repeat these analyses using dimensional measures. We conclude that when aiming to understand the structure and correlates of psychopathology it is important to (a) look beyond model fit indices to choose between different models, (b) examine the reliability of latent variables directly, and (c) be cautious when isolating and interpreting the unique effects of specific psychopathology factors, regardless of which model is used. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/abn0000533 | DOI Listing |
Health Inf Manag
September 2025
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The success of disease registry systems (DRSs) depends on developing software that aligns with the registry's specific needs.
Objective: This study focuses on localising the Checklist with Items for Patient Registry sOftware Systems (CIPROS) to facilitate the DRS assessment.
Method: This applied and cross-sectional study was carried out in 2023 in six phases.
Scand J Rheumatol
September 2025
The Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Frederiksberg, Denmark.
Objective: Pain hypersensitivity and hypersensitivity to other sensory modalities (visual, auditory, olfactory, and tactile) are considered defining features in nociplastic pain states. A self-report measure of sensory sensitivity may help to characterize sensory profiles across pain populations. This study aimed to evaluate the psychometric properties of a newly developed Danish nine-item Sensory Sensitivity Profile (SSP) questionnaire in patients with fibromyalgia.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.
Background: Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
Methods: RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes.
Int J Chron Obstruct Pulmon Dis
September 2025
The First Clinical Medical College of Lanzhou University, Lanzhou, People's Republic of China.
Chronic Obstructive Pulmonary Disease (COPD) is a prevalent chronic respiratory disorder characterized by airway inflammation and irreversible airflow limitation. Its marked heterogeneity and complexity pose significant challenges to traditional clinical assessments in terms of prognostic prediction and personalized management. In recent years, the exploration of biomarkers has opened new avenues for the precise evaluation of COPD, particularly through multi-biomarker prediction models and integrative multimodal data strategies, which have substantially improved the accuracy and reliability of prognostic assessments.
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