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Context: Dental schools seek to educate students to become inclined toward self-directed, lifelong learning, an important mindset for healthcare professionals that may be linked to deep versus surface learning approaches. Students using a deep learning approach are more intrinsically motivated and actively engage in higher-order thinking, while those using a surface learning approach are more extrinsically motivated and aim for passive learning.
Objectives: Because student learning approaches can be influenced by a wide variety of learning experiences, we sought to understand how student approaches to learning differ by year in dental school and are related to academic achievement.
Methods: A total of 244 students in a 4-year dental school program in South Korea voluntarily participated in this study. We collected data on school year and academic achievement, and approaches to learning of participants using the validated Study Processes Questionnaire to assess learning approach, which included the constructs of deep motive, deep strategy, surface motive, and surface strategy.
Results: We conducted 3 sets of statistical analyses and found that most students adopted a deep approach to learning (DAL) in their first and second years (Y1 and Y2), with third-year students (Y3) showing heavy dependence on a surface approach to learning (SAL) and sharp drops in intrinsic motives. Student approaches to learning were not significantly related to academic achievement. In the first 2 years of dental school, students tended to adopt a DAL, and viewed their learning as personal growth and their profession as necessitating deep intellectual inquiry.
Conclusions: In the third year, the change from a DAL to a SAL coincided with entry to clinical training. The lack of integration of biomedical science (Y1 and Y2) and clinical science (Y3 and Y4), and increased stress in the initial clinical context may account for this difference. The poor correlation between a DAL and high achievement may indicate a need for change in assessment methods. This study hopes to stimulate reflection regarding student learning approaches and educational efforts that prepare future dentists for lifelong learning.
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http://dx.doi.org/10.1002/jdd.12043 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
Department of Food Science and Engineering, Ningbo University, Ningbo 315211, P.R. China.
Sleep deprivation (SD) is a major contributor to cognitive impairment, often accompanied by central neuroinflammation and gut microbiota dysbiosis. The tryptophan (TRP) pathway, activated via indoleamine 2,3-dioxygenase (IDO), serves as a critical link between immune activation and neuronal damage. Umbelliferone (UMB), a naturally occurring coumarin compound, possesses anti-inflammatory, antioxidant, and microbiota-modulating properties.
View Article and Find Full Text PDF