The objective of this replication study was to compare the perspectives of Hebrew-speaking speech-language pathologists (SLPs) on augmentative and alternative communication (AAC) assessment and intervention in each of the five language domains (semantics, pragmatics, phonology, morphology, and syntax) with those previously reported for English-speaking SLPs. Specifically, the comparison aimed to understand AAC service delivery patterns in different linguistic contexts. Using an anonymous online survey, the study collected responses from 167 Hebrew-speaking SLPs regarding preprofessional training, clinical practices, resource adequacy and continuing education interests related to AAC assessment and intervention in each language domain. Global agreement was found among Hebrew-speaking and those previously reported for English-speaking SLPs on the importance of all language domains for people who use AAC (PWUAAC) and their interest in professional development. In ratings of preprofessional training, clinical practice, and resource adequacy, pragmatics and semantics had consistently higher percentages of positive responses in both groups, followed by syntax, while morphology and phonology received fewest. Fewer Hebrew-speaking as compared to English-speaking SLPs rated morphology/phonology skills as important for PWUAAC and reported providing clinical services in each language domain. However, more Hebrew-speaking SLPs rated their resources and preprofessional training as adequate in semantics, pragmatics, syntax, and phonology. These findings suggest that while shared AAC service delivery patterns exist in different linguistic contexts (e.g., Hebrew, English) across language domains, there is a need for development and validation of language-specific (e.g., Hebrew) resources, particularly in morphology and phonology. Factors influencing clinical decision-making, including client age, preferences, disabilities, and resource availability, are also discussed.
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Diagnostics (Basel)
March 2025
Department of Computer Science, Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang 26600, Malaysia.
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March 2025
Faculty of Education, Shenzhen University, Shenzhen, China.
Inspired by self-determination theory (SDT), the Academic Motivation Scale (AMS) was developed to measure students' learning motivation. While the AMS has been widely validated and used in educational contexts, it has generally overlooked the domain-specific nature of academic motivation, particularly in learning English as a foreign language (EFL) in China, home to the world's largest population of EFL learners. This study sought to adapt the AMS and substantiate its validity using both within-network and between-network approaches with a sample of 1,390 Chinese secondary EFL learners.
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March 2025
Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, The Netherlands; Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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March 2025
School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200230, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address:
Magnetic Resonance Imaging (MRI) has become a pivotal tool in diagnosing brain diseases, with a wide array of computer-aided artificial intelligence methods being proposed to enhance diagnostic accuracy. However, early studies were often limited by small-scale datasets and a narrow range of disease types, which posed challenges in model generalization. This study presents UniBrain, a hierarchical knowledge-enhanced pre-training framework designed for universal brain MRI diagnosis.
View Article and Find Full Text PDFMed Image Anal
March 2025
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China. Electronic address:
Text-guided visual understanding is a potential solution for downstream task learning in echocardiography. It can reduce reliance on labeled large datasets and facilitate learning clinical tasks. This is because the text can embed highly condensed clinical information into predictions for visual tasks.
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