Background: The aim of this study is to review the literature on known barriers and solutions that face educators when developing and implementing online learning programs for medical students and postgraduate trainees.
Methods: An integrative review was conducted over a three-month period by an inter-institutional research team. The search included ScienceDirect, Scopus, BioMedical, PubMed, Medline (EBSCO & Ovid), ERIC, LISA, EBSCO, Google Scholar, ProQuest A&I, ProQuest UK & Ireland, UL Institutional Repository (IR), UCDIR and the All Aboard Report. Search terms included online learning, medical educators, development, barriers, solutions and digital literacy. The search was carried out by two reviewers. Titles and abstracts were screened independently and reviewed with inclusion/exclusion criteria. A consensus was drawn on which articles were included. Data appraisal was performed using the Critical Appraisal Skills Programme (CASP) Qualitative Research Checklist and NHMRC Appraisal Evidence Matrix. Data extraction was completed using the Cochrane Data Extraction Form and a modified extraction tool.
Results: Of the 3101 abstracts identified from the search, ten full-text papers met the inclusion criteria. Data extraction was completed on seven papers of high methodological quality and on three lower quality papers. Findings suggest that the key barriers which affect the development and implementation of online learning in medical education include time constraints, poor technical skills, inadequate infrastructure, absence of institutional strategies and support and negative attitudes of all involved. Solutions to these include improved educator skills, incentives and reward for the time involved with development and delivery of online content, improved institutional strategies and support and positive attitude amongst all those involved in the development and delivery of online content.
Conclusion: This review has identified barriers and solutions amongst medical educators to the implementation of online learning in medical education. Results can be used to inform institutional and educator practice in the development of further online learning.
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http://dx.doi.org/10.1186/s12909-018-1240-0 | DOI Listing |
J Chem Inf Model
December 2024
School of Physics, Shandong University, Jinan 250100, China.
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad application prospects, for instance, drug design and enzyme engineering. In this study, we evaluated the robustness of a DL-based ligand conformation optimization protocol (DeepRMSD+Vina) for optimizing structures with input perturbations by examining the predicted ligand binding poses and scoring.
View Article and Find Full Text PDFEur Child Adolesc Psychiatry
December 2024
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
Online social interactions increase into adolescence. Although cross-sectional studies have positively associated online social activity (OSA) time and attention-deficit/hyperactivity disorder (ADHD) problems, the directionality remains unclear. Therefore, we examined longitudinal associations between OSA time and ADHD problems using data from the Adolescent Brain Cognitive Development (ABCD) study.
View Article and Find Full Text PDFEnviron Sci Technol
December 2024
Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States.
Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the applicability domain (AD) has been significantly limited. To address this limitation, we curated a much larger experimental data set (Primary data set), which contains 2358 kinetic records.
View Article and Find Full Text PDFPhenomics
October 2024
School of Kinesiology, Shanghai University of Sport, Qingyuanhuan Road, #650, Yangpu District, Shanghai, 200438 China.
Unlabelled: The field of competitive swimming lacks broadly applicable predictive models for talent identification across various age groups of adolescent swimmers. This study aimed to construct a predictive model for athletic talent using machine learning methods based on anthropometric and physiological data. Baseline data were collected from 5444 participants aged 10-18 in Shanghai, China, between 2015 and 2018, with 4969 completing a 3-year follow-up.
View Article and Find Full Text PDFFront Microbiol
December 2024
College of Water Sciences, Beijing Normal University, Beijing, China.
Sediments are key reservoirs for rare bacterial biospheres that provide broad ecological services and resilience in riverine ecosystems. Compared with planktons, there is a lack of knowledge regarding the ecological differences between abundant and rare taxa in benthic bacteria along a large river. Here, we offer comprehensive insights into the spatiotemporal distributions, co-occurrence networks, and assembly processes of three divided categories namely always rare taxa (ART), conditionally rare taxa (CRT), and conditionally rare and abundant taxa (CRAT) in sediments covering a distance of 4,300 km in the Yangtze River.
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