Mobile technologies (including handheld and wearable devices) have the potential to enhance learning activities from basic medical undergraduate education through residency and beyond. In order to use these technologies successfully, medical educators need to be aware of the underpinning socio-theoretical concepts that influence their usage, the pre-clinical and clinical educational environment in which the educational activities occur, and the practical possibilities and limitations of their usage. This Guide builds upon the previous AMEE Guide to e-Learning in medical education by providing medical teachers with conceptual frameworks and practical examples of using mobile technologies in medical education. The goal is to help medical teachers to use these concepts and technologies at all levels of medical education to improve the education of medical and healthcare personnel, and ultimately contribute to improved patient healthcare. This Guide begins by reviewing some of the technological changes that have occurred in recent years, and then examines the theoretical basis (both social and educational) for understanding mobile technology usage. From there, the Guide progresses through a hierarchy of institutional, teacher and learner needs, identifying issues, problems and solutions for the effective use of mobile technology in medical education. This Guide ends with a brief look to the future.
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http://dx.doi.org/10.3109/0142159X.2016.1141190 | DOI Listing |
J Hematol Oncol
January 2025
Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
The tumor microenvironment (TME) is integral to cancer progression, impacting metastasis and treatment response. It consists of diverse cell types, extracellular matrix components, and signaling molecules that interact to promote tumor growth and therapeutic resistance. Elucidating the intricate interactions between cancer cells and the TME is crucial in understanding cancer progression and therapeutic challenges.
View Article and Find Full Text PDFJ Health Popul Nutr
January 2025
Student Research Committee, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Background: Socioeconomic inequality in nutritional status as one of the main social determinants of health can lead to inequality in health outcomes. In the present study, the socioeconomic inequality in the burden of nutritional deficiencies among the countries of the world using Global Burden of Disease (GBD) data was investigated.
Methods: Burden data of nutritional deficiencies and its subsets including protein-energy malnutrition, iodine deficiency, vitamin A deficiency, and dietary iron deficiency form GBD study and Human Development Index (HDI), a proxy for the socio-economic status of countries, from united nations database were collected.
BMC Nutr
January 2025
Department of Epidemiology and Biostatistics, Tbilisi State Medical University, Tbilisi, Georgia.
Background: Childhood overweight and obesity are significant global public health challenges that affect approximately 340 million children worldwide. In Georgia, the prevalence of childhood obesity is alarming, with approximately 28% of 7-year-old children classified as overweight or obese in 2019. This study aimed to investigate the key factors associated with overweight and obesity among school-age children in Georgia.
View Article and Find Full Text PDFBMC Nutr
January 2025
Centre for Lifecourse Nutrition, Department of Nutrition and Public Health, Faculty of Health and Sport Sciences, University of Agder, Postbox 422, Kristiansand, 4604, Norway.
Background: Early Childhood Education and Care (ECEC) centers play an important role in fostering healthy dietary habits. The Nutrition Now project focusing on improving dietary habits during the first 1000 days of life. Central to the project is the implementation of an e-learning resource aimed at promoting feeding practices among staff and healthy dietary behaviours for children aged 0-3 years in ECEC.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Stem Cell and Regenerative Medicine, Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
Background: It is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).
Methods: The xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis.
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