Background: Health care experts need high levels of competence, yet there is little evidence on the influence of digital learning on health science students' competence development.
Objectives: This study aims to describe health sciences students' experiences of the development of their competence and the influences of digital learning upon their competence.
Design: A qualitative descriptive research.
Participants: A total of 15 health sciences students were interviewed.
Methods: The data was collected by using individual semi-structured interviews during the spring of 2021. The data was analyzed using content analysis.
Results: The health sciences students felt that their expertise encompasses motivation for future career development, understanding the social and professional influences on their career development, versatile expertise in various aspects of health sciences, and developing competence in different learning environments. The students recognized that digital learning requires the active participation, digitalization is a part of a successful learning environment, and digital learning challenges social interactions. The students' digital learning facilitated competence development, which broadened their understanding of skills relevant to health sciences; however, these benefits could only be obtained when including adequate support.
Conclusions: The results hold social value for the development of health sciences education as policy-makers can use the presented information to develop high-quality, digital learning procedures.
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http://dx.doi.org/10.1016/j.nedt.2022.105635 | DOI Listing |
BMC Med Inform Decis Mak
January 2025
QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Department of Radiology Oncology, Emory University, Clifton Rd, Atlanta, Georgia, 30322-1007, UNITED STATES.
This study aims to develop a digital twin (DT) framework to achieve adaptive proton prostate stereotactic body radiation therapy (SBRT) with fast treatment plan selection and patient-specific clinical target volume (CTV) setup uncertainty. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes.
View Article and Find Full Text PDFNat Med
January 2025
Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFHealth Aff (Millwood)
January 2025
Jordan Everson, Office of the Assistant Secretary for Technology Policy, Washington, D.C.
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling.
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