Assessments should accurately predict future performance in a wide variety of settings yet be feasible to conduct. In medical education a robust and comprehensive system of assessment is essential to protect the public from inadequate professionals. The parameters for devising such an assessment are well-defined, and good practice for writing examinations well-established. However even excellent written assessments are limited in their predictive validity, and limited in sampling, face and construct validity. The increasing availability and power of computing has led to growing interest in computer simulations for use in examinations, creating assessment virtual patients (AVPs). They can potentially test knowledge and data interpretation, incorporate images, sound or video and test decision making. Such AVPs could represent the most comprehensive, integrated assessment possible that is both objective and feasible. This article focuses on AVP design, distinguishing between linear and branched models, choice and consequence driven designs. It reviews the use of AVPs in the context of assessment theory. It presents different AVP designs discussing their benefits and problems. AVPs can become valuable components in high stakes medical exams, particularly in later years of courses. However this requires application of established assessment principles to AVP design.
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http://dx.doi.org/10.1080/01421590903134152 | DOI Listing |
Nurse Educ
October 2024
Author Affiliations: The Ohio State University College of Nursing, Columbus, Ohio (Dr Hoying, Mss Terry and Gray-Bauer, and Dr Melnyk); and The University of Arizona College of Nursing, Tucson, Arizona (Dr Kelly).
Background: Nursing students experience significantly more stress related diseases when compared to non-nursing students, and the state of their mental health can result in short-term increased attrition rates and increased nursing shortages.
Purpose: A preexperimental pre-post study design was used to examine mental health and healthy behaviors among prenursing students.
Methods: Cohorts received the MINDSTRONG© program either in-person or virtually.
NPJ Prim Care Respir Med
December 2024
ResMed Science Center, San Diego, CA, USA.
Digital health platforms for asthma self-management have demonstrated promise in improving clinical and quality of life outcomes. However, few studies have examined such an approach in a real-world, fully remote setting. As such, we evaluated the benefit of an evidence-based digital self-management platform for asthma-both on its own and when integrated into an established virtual clinical service.
View Article and Find Full Text PDFTo achieve carbon neutrality, solar photovoltaic (PV) in China has undergone enormous development over the past few years. PV datasets with high accuracy and fine temporal span are crucial to assess the corresponding carbon reductions. In this study, we employed the random forest classifier to extract PV installations throughout China in 2015 and 2020 using Landsat-8 imagery in Google Earth Engine.
View Article and Find Full Text PDFBMC Geriatr
December 2024
ZHAW Zurich University of Applied Sciences, Winterthur Institute of Health Economics, Winterthur, Switzerland.
Background: Fall prevention programmes are essential interventions in societies with aging populations. This study assessed the fall rate and other health outcomes, as well as the cost-effectiveness of a home-based fall prevention programme for community-dwelling older people. In a single home visit, trained physical or occupational therapists performed fall risk assessments, eliminated environmental risk factors, and provided tailored exercises.
View Article and Find Full Text PDFAcad Radiol
December 2024
Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL (M.H-S., H.S.S., A.G.R., S.E.M., J.C.P., E.Y.A., B.H., R.F.); Department of Radiology, University of Florida College of Medicine, Gainesville, FL (M.H-S., H.S.S., A.G.R., J.C.P., E.Y.A., B.H., R.F.); Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL (R.F.); Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL (R.F.); Department of Otolaryngology - Head and Neck Surgery, McGill University, Montreal, Quebec, Canada (R.F.); Department of Radiology, AdventHealth Medical Group, Maitland, FL (R.F.). Electronic address:
Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).
Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction.
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