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Continuous training based on the needs of operating room nurses using web application: a new approach to improve their knowledge.
BMC Med Educ
March 2024
Department of the Operating Room, Nursing and Midwifery Care Research Centre, School of Nursing and Midwifery, Isfahan University of Medical Sciences, Isfahan, Iran.
Introduction: Since university education and intensive and limited pre-service training do not provide an acceptable level of performing the duties of operating room nurses, and considering the limitations of traditional training methods in the field of operating room; This study was conducted with the aim of determining the effect of using the electronic education approach based on web application, leveled, personalized and based on the needs of nurses on their level of knowledge and satisfaction.
Materials And Methods: This research is a quasi-experimental type of single-group multi-center pre-test-post-test, which during that, four stages of educational needs assessment, educational content design, web application design for training and evaluation of operating room nurses and determining the effectiveness of this method are included. Based on their knowledge and satisfaction, during this period, 36 nurses from the operating rooms that met the study criteria were included in the study by stratified random sampling based on the determined sample size.
Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.
PLoS One
November 2023
Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Dhaka, Bangladesh.
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people's lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond.
View Article and Find Full Text PDFA Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners' Affective States.
Sensors (Basel)
April 2023
Research Center for Computing and Multimedia Studies, Hosei University, Tokyo 102-8160, Japan.
Students' affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students' affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students.
View Article and Find Full Text PDFA Large-Scale, Multiplayer Virtual Reality Deployment: A Novel Approach to Distance Education in Human Anatomy.
Med Sci Educ
April 2023
Fort Collins, CO USA Department of Biomedical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University.
Unlabelled: The arrival of COVID-19 restrictions and the increasing demand of online instruction options posed challenges to education communities worldwide, especially in human anatomy. In response, Colorado State University developed and deployed an 8-week-long large-scale virtual reality (VR) course to supplement online human anatomy instruction. Students ( = 75) received a VR-capable laptop and head-mounted display and participated in weekly synchronous group laboratory sessions with instructors.
View Article and Find Full Text PDFOn cheap entropy-sparsified regression learning.
Proc Natl Acad Sci U S A
January 2023
Institute of Human Genetics, University Medical Center Johannes Gutenberg-Universität (JGU) Mainz, Anselm-Franz-von-Bentzel-Weg 3 55128, Mainz, Germany.
Regression learning is one of the long-standing problems in statistics, machine learning, and deep learning (DL). We show that writing this problem as a probabilistic expectation over (unknown) feature probabilities - thus increasing the number of unknown parameters and seemingly making the problem more complex-actually leads to its simplification, and allows incorporating the physical principle of entropy maximization. It helps decompose a very general setting of this learning problem (including discretization, feature selection, and learning multiple piece-wise linear regressions) into an iterative sequence of simple substeps, which are either analytically solvable or cheaply computable through an efficient second-order numerical solver with a sublinear cost scaling.
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