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Objectives: This research used text mining to determine the impact of curricular experiences in each year of study on the formation of professional identity among students aspiring to become physical therapists (PTs) and occupational therapists (OTs).
Methods: This study included 210 students (126 PT and 84 OT) enrolled at a single rehabilitation university in Japan in 2020 and 2021. These participants completed an open-ended questionnaire on personal growth 6, 12, 18 and 24 months after enrollment. Text mining was used to extract frequently occurring nouns from descriptive text data at these four time points. A hierarchical cluster analysis was then performed to generate clusters. The number of students mentioning at least one noun in each cluster was counted, and the proportion of students in each cluster was compared across the four time periods using Cochran's Q test.
Results: The 16 nouns that appeared more than 30 times in 1073 sentences were classified into four clusters: "action plan for passing the credit certification examination", "communication skill", "medical knowledge" and "school life with clinical practice in mind". The proportion of students belonging to the four clusters varied across periods. "Action plan for passing the credit certification examination" and "communication skill" differed significantly across periods (p<0.0001 and p=0.0008, respectively).
Conclusions: The results reveal how students have grown in their curriculum. Their growth was transformative.
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http://dx.doi.org/10.20407/fmj.2024-024 | DOI Listing |
Am J Epidemiol
September 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects. TBSS traditionally analyze claims data with outcomes defined via diagnosis codes. TBSS have not been previously applied to rich clinical information in Electronic Health Records (EHR).
View Article and Find Full Text PDFJ Hazard Mater
September 2025
Mining and Minerals Engineering, Virginia Tech, Blacksburg, VA, USA. Electronic address:
Occupational lung disease remains a serious concern among miner workers, underscoring the need for improved characterization of respirable dust. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) enables high-resolution analysis of filter samples, but accurate identification of complex, multi-constituent particles like agglomerates during direct-on-filter (DOF) analysis remains challenging. This is because standard tools for automated SEM-EDX treat each dust entity as an independent particle.
View Article and Find Full Text PDFComput Biol Chem
August 2025
Department of Computer Science, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh 530045, India. Electronic address:
-Aspect-Based Sentiment Analysis (ABSA) is considered a unique variant, which intends to identify the opinions regarding delicate topics. However, it is a neglected topic of study, ABSA attempts to find out the sentiment polarity on particular characteristics within statements, enabling more precise mining of consumers' emotional polarities regarding various aspects. The conversion of the conventional rating-aided recommendation approach into an effective aspect-aided procedure is made easier by this evaluation.
View Article and Find Full Text PDFJ Adv Res
September 2025
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology at Beijing, Beijing 100083, China. Electronic address:
Introduction: Accurate characterization of multi-size fractures in coal is crucial for estimating its transport properties. However, the extraction of narrow microfractures in 3D voxel-type CT images is difficult, which causes the loss of connectivity in the extracted fracture network and reduces the accuracy of the predicted transport properties.
Objectives: Improving the image quality and optimizing the segmentation process to deal with the inaccuracy of fracture extraction from coal CT images.
Cell Rep Methods
August 2025
Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06511, USA; Department of Biostatistics, Yale University, New Haven, CT 06511, USA. Electronic address:
Single-cell multi-modal data integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline and evaluate the contributions of data integration. In this article, we revisit the definition and contributions of multi-modal data integration and propose a strong and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration.
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