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Nurse Educ Today
January 2025
School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China. Electronic address:
Background: Clinical practice is key in the development and enhancement of the professional competencies for Master of Nursing Specialist postgraduates in anesthesia; however, there is a lack of unified and standardized clinical practice training programs in China, failing to guarantee teaching quality among institutions.
Objective: To understand perceptions of the clinical practice training program setting for Master of Nursing Specialist postgraduates in anesthesia from the dual perspectives of faculty and students.
Design: A qualitative descriptive study.
BMC Psychiatry
January 2025
Division of Epidemiology and Social Sciences, Institute for Health and Equity, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA.
Background: During adolescence, a critical developmental phase, cognitive, psychological, and social states interact with the environment to influence behaviors like decision-making and social interactions. Depressive symptoms are more prevalent in adolescents than in other age groups which may affect socio-emotional and behavioral development including academic achievement. Here, we determined the association between depression symptom severity and behavioral impairment among adolescents enrolled in secondary schools of Eastern and Central Uganda.
View Article and Find Full Text PDFJ Colloid Interface Sci
April 2025
College of Physics, Qingdao University, Qingdao 266071, China. Electronic address:
Polyacrylonitrile (PAN)-based composite solid electrolytes (CSEs) hold great promise in the practical deployment of solid lithium batteries (SLBs) owing to their high voltage stability but suffer from poor stability against Li-metal. Herein, a poly(1,3-dioxolane) (PDOL)-graphitic CN (g-CN, i.e.
View Article and Find Full Text PDFNeural Netw
January 2025
Luca Healthcare R&D, Shanghai, 200000, China. Electronic address:
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predominantly addressed classification tasks rather than regression ones. To address these gaps, our analysis delves into the generalisation error bound of the target model, aiming to understand the intrinsic limitations of pseudo-label-based SFUDA methods.
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