Objective: To translate and validate the Clinically Useful Depression Outcome Scale for Urdu-speaking population.
Methods: The cross-sectional study was conducted in Rawalpindi and Islamabad from January 2018 to November 2019. The process of translation and validation was conducted in two phases. In the first phase, the scale was forward and backward translated. In the second phase two validation studies were conducted; one for computing Cronbach's alpha, test-retest reliability, and item-total correlation, and exploring convergent and discriminant validity; and the other for exploring linguistic equivalence between the original and the translated scale. Data was analysed using SPSS 22.
Results: The first validation study had 170 subjects; 85(50%) in clinical and 85(50%) in non-clinical settings. The translated scale was found to be internally consistent, and convergent and discriminant validity coefficients were significant (p<0.05). Mean difference between clinical and non-clinical groups was also significant (p<0.05), indicating the diagnostic capability of the translated scale. The second validation study, conducted on a separate sample of 82 bilingual participants, showed that the mean difference between the original and the translated version was non-significant (p>0.05), indicating that the Urdu version can be considered an equivalent to the original scale.
Conclusions: The translated version of the Clinically Useful Depression Outcome Scale (CUDOS-Urdu) was found to be a reliable and valid instrument for measuring depressive symptoms in Urdu-speaking individuals.
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http://dx.doi.org/10.47391/JPMA.382 | DOI Listing |
PLoS One
January 2025
Department of Clinical Psychology, University of Dhaka, Bangladesh.
Background: The absence of a reliable and valid Bangla instrument for measuring somatic symptom disorder hinders research and clinical activities in Bangladesh. The present study aimed at translating and validating the Somatic Symptom Disorder-B criteria (SSD-12).
Method: A cross-sectional design was used with purposively selected clinical (n = 100) and non-clinical (n = 100) samples.
PLoS One
January 2025
Department of Psychology, Crean College of Health and Behavioral Sciences, Chapman University, Orange, California, United States of America.
Accumulating evidence indicates that unpredictable signals in early life represent a unique form of adverse childhood experiences (ACEs) associated with disrupted neurodevelopmental trajectories in children and adolescents. The Questionnaire of Unpredictability in Childhood (QUIC) was developed to assess early life unpredictability [1], encompassing social, emotional, and physical unpredictability in a child's environment, and has been validated in three independent cohorts. However, the importance of identifying ACEs in diverse populations, including non-English speaking groups, necessitates translation of the QUIC.
View Article and Find Full Text PDFPLoS One
January 2025
Center for Innovation in Brain Science, University of Arizona Health Sciences, Tucson, Arizona, United States of America.
Translational validity of mouse models of Alzheimer's disease (AD) is variable. Because change in weight is a well-documented precursor of AD, we investigated whether diversity of human AD risk weight phenotypes was evident in a longitudinally characterized cohort of 1,196 female and male humanized APOE (hAPOE) mice, monitored up to 28 months of age which is equivalent to 81 human years. Autoregressive Hidden Markov Model (AHMM) incorporating age, sex, and APOE genotype was employed to identify emergent weight trajectories and phenotypes.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
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