Background: Changing the course duration or timing of subjects in learning pathways would influence medical students' learning outcomes. Curriculum designers need to consider the strategy of reducing cognitive load and evaluate it continuously. Our institution underwent gradual curricular changes characterized by reducing cognitive load since 2000. Therefore, we wanted to explore the impact of this strategy on our previous cohorts.
Methods: This cohort study explored learning pathways across academic years of more than a decade since 2000. Eight hundred eighty-two medical students between 2006 and 2012 were included eventually. Learning outcomes included an average and individual scores of subjects in different stages. Core subjects were identified as those where changes in duration or timing would influence learning outcomes and constitute different learning pathways. We examined whether the promising learning pathway defined as the pathway with the most features of reducing cognitive load has higher learning outcomes than other learning pathways in the exploring dataset. The relationship between features and learning outcomes was validated by learning pathways selected in the remaining dataset.
Results: We found nine core subjects, constituting four different learning pathways. Two features of extended course duration and increased proximity between core subjects of basic science and clinical medicine were identified in the promising learning pathway 2012, which also had the highest learning outcomes. Other pathways had some of the features, and pathway 2006 without such features had the lowest learning outcomes. The relationship between higher learning outcomes and cognitive load-reducing features was validated by comparing learning outcomes in two pathways with and without similar features of the promising learning pathway.
Conclusion: An approach to finding a promising learning pathway facilitating students' learning outcomes was validated. Curricular designers may implement similar design to explore the promising learning pathway while considering potential confounding factors, including students, medical educators, and learning design of the course.
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http://dx.doi.org/10.1097/JCMA.0000000000001116 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
J Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
PLoS One
January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Biological Sciences and Biotechnology, College of Natural Sciences, Chungbuk National University, Cheongju, Chungbuk, 28644, Republic of Korea.
Antimicrobial peptides (AMPs) are promising agents for treating antibiotic-resistant bacterial infections. Although discovering novel AMPs is crucial for combating multidrug-resistant bacteria and biofilm-related infections, their clinical potential relies on precise, real-time evaluation of efficacy, toxicity, and mechanisms. Optical diffraction tomography (ODT), a label-free imaging technology, enables real-time visualization of bacterial morphological changes, membrane damage, and biofilm formation over time.
View Article and Find Full Text PDFCell Rep
January 2025
Department of Biology, Boston University, Boston, MA 02215, USA; Center for Neurophotonics, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Center for Systems Neuroscience, Boston University, Boston MA 02215, USA. Electronic address:
Task learning involves learning associations between stimuli and outcomes and storing these relationships in memory. While this information can be reliably decoded from population activity, individual neurons encoding this representation can drift over time. The circuit or molecular mechanisms underlying this drift and its role in learning are unclear.
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