The aims of this study were to assess students' perceptions of their educational environment and approaches to learning, and determine if perceptions of learning environment associates with approaches to learning. A survey was conducted to collect data from a regional private university in Indonesia. A total of 232 nursing students completed two questionnaires that measured their perceptions of educational environment and approaches to learning. The measurement was based on Dundee Ready Education Environment Measurement (DREEM) and Approaches and Study Skills Inventory for Students (ASSIST). Five learning environments dimensions and three learning approaches dimensions from two measures were measured. The overall score of DREEM was 131.03/200 (SD 17.04), it was in the range considered to be favourable. The overall score is different significantly between years of study (p value = 0.01). This study indicated that the majority of undergraduate nursing students' adopt strategic approach (n = 139. 59.9%). The finding showed that perceived educational environment significantly associated with approaches to learning. This study implicated the need to maintain conducive learning environment. There is also a need to improve the management of learning activities that reflect the use of student-centered learning.
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http://dx.doi.org/10.1016/j.nepr.2014.08.009 | DOI Listing |
EClinicalMedicine
December 2024
Department of Pathology and Genetics, Laboratory of Cancer Medical Science, Hokuto Hospital, Obihiro, Hokkaido, Japan.
Background: Pancreatic cancer is highly aggressive and has a low survival rate primarily due to late-stage diagnosis and the lack of effective early detection methods. We introduce here a novel, noninvasive urinary extracellular vesicle miRNA-based assay for the detection of pancreatic cancer from early to late stages.
Methods: From September 2019 to July 2023, Urine samples were collected from patients with pancreatic cancer (n = 153) from five distinct sites (Hokuto Hospital, Kawasaki Medical School Hospital, National Cancer Center Hospital, Kagoshima University Hospital, and Kumagaya General Hospital) and non-cancer participants (n = 309) from two separate sites (Hokuto Hospital and Omiya City Clinic).
World J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFFront Artif Intell
December 2024
Alimentary Tract Research Center, Clinical Sciences Research Institute, Imam Khomeini Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data.
View Article and Find Full Text PDFBiofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy.
View Article and Find Full Text PDFPurpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics' robustness to typical pre-processing techniques.
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