This paper explores interprofessional learning (IPL) and whether it would profit from a more systematic merger with problem-based learning (PBL). IPL is based on the idea of bringing together knowledge from the different health professions as they interact with each other for better health care. PBL springs from the idea of bringing learning closer to the application of knowledge in every day life. It has been widely adopted as an interprofessional learning method, for example at Linkøping in Sweden.
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http://dx.doi.org/10.1080/13561820903163579 | DOI Listing |
Orphanet J Rare Dis
January 2025
Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus, Groningen, 30001 - 9700 RB, the Netherlands.
Background: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on a combination of different biomarkers, mostly routine clinical chemical markers and subsequent genetic confirmation.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Department of Rural Health, Melbourne Medical School, The University of Melbourne, 49 Graham Street, Shepparton, VIC, 3630, Australia.
The health disparities between rural and urban populations in Australia, driven by socioeconomic, environmental, and healthcare access factors, highlight the urgent need for rural-focused medical education. The Melbourne Medical School's Rural Health Discovery program addresses this need by integrating adult learning principles within a redesigned curriculum that includes the Rural Health Foundations and Integrating Rural Health topics. These Discovery topics engage medical students from diverse backgrounds through a blend of self-directed learning, problem-solving, and immersive clinical placements in rural settings.
View Article and Find Full Text PDFPhys Med Biol
January 2025
North China Electric Power University - Baoding Campus, North China Electric Power University, Baoding, Hebei Province, P.R.China, Baoding, Hebei, 071003, CHINA.
Objective: The optical absorption properties of biological tissues in photoacoustic tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve iterative optimization. However, these methods are hindered by several challenges, including high computational demands, the need for regularization, and sensitivity to both the accuracy of the forward model and the completeness of the measurement data.
View Article and Find Full Text PDFComput Biol Med
January 2025
Auckland Bioengineering Institute, The University of Auckland, New Zealand. Electronic address:
Problem: Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's complex pathogenesis.
Aim: Conduct a systematic review to investigate how ML/DL techniques have influenced the progression of brain tumour histopathological research, particularly in GBM.
Methods: 54 eligible studies were collected from the PubMed and ScienceDirect databases, and their information about the types of brain tumour/s used, types of -omics data used with histopathological data, origins of the data, types of ML/DL and its training and evaluation methodologies, and the ML/DL task it was set to perform in the study were extracted to inform us of trends in GBM-related ML/DL-based research.
Self-regulated learning (SRL) has been regarded as one of the indispensable factors affecting students' academic success in online learning environments. However, the current understanding of the mechanism/causes of SRL in online ill-structured problem-solving remains insufficient. This study, therefore, examines the configural causal effects of goal attributes, motivational beliefs, creativity, and grit on self-regulated learning.
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