Part 1 of the AMEE Guide focused on foundational concepts such as theory, methods, and instructional design in online learning. Part 2 builds upon Part 1, introducing technology tools and applications of these foundational concepts by exploring the various levels (from beginner to advanced) of utilisation, while describing how their usage can transform Health Professions Education. This Part covers Learning Management Systems, infographics, podcasting, videos, websites, social media, online discussion forums, simulation, virtual patients, extended and virtual reality. Intertwined are other topics, such as online small group teaching, game-based learning, FOAM, online social and collaboration learning, and virtual care teaching. We end by discussing digital scholarship and emerging technologies. Combined with Part 1, the overall aim of Part 2 is to produce a comprehensive overview to help guide effective use online learning in Health Professions Education.
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http://dx.doi.org/10.1080/0142159X.2023.2259069 | DOI Listing |
Australas J Ageing
January 2025
Department of Psychological Sciences, Swinburne University, Melbourne, Victoria, Australia.
Objectives: There are limited mental health support services in Australia that address the well-being of family members of aged care residents. The aim of this project was to evaluate the feasibility, acceptability and preliminary effectiveness of an online program designed to support residents' families.
Methods: This one-arm mixed methods project examined uptake, attendance and retention patterns, satisfaction and experience with the service, and pre- and postoutcomes with respect to depressive and anxiety symptoms and loneliness.
Ecotoxicol Environ Saf
January 2025
Department of Pharmacy, Jieyang People's Hospital, Jieyang, China.
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk.
View Article and Find Full Text PDFAdv Life Course Res
January 2025
Department of Human Geography, Stockholm University, Sweden.
Research on the consequences of residential mobility for educational outcomes is inconclusive about when and for whom moving is detrimental or beneficial. Whether moving during childhood impacts educational attainment depends on how often, how far and at which age one moves; and on whether the neighbourhood conditions improve or decline with the move. This study aims to better understand under which circumstances moving during childhood impacts educational attainment by studying residential mobility and neighbourhood trajectories of children born in different types of neighbourhoods and how this is associated with completion of tertiary education.
View Article and Find Full Text PDFJ Surg Educ
January 2025
Department of Family Medicine, Cleveland Clinic, Cleveland, OH.
Objective: As the number of women in medical training rises, there has been increased interest in understanding the perspectives of minority women. Although Muslim women face unique challenges in pursuing medical training, there are no current studies dedicated to understanding the experience of Muslim women as healthcare professionals. This study aims to present insight into perspectives of United States-based Muslim women physicians, residents, and medical students on discrimination and allyship, the operating room environment, mentorship, and institutional resources.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Pathology and Department of Immunobiology, Yale School of Medicine.
Summary: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.
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