The eruption of the COVID-19 pandemic forced many universities to quickly transition traditional in-person laboratory courses to an online format for remote learning. Consequently, learning objectives focused on hands-on laboratory skills shifted to ones that assess skills that could be recapitulated in the online format. We have transitioned a staple experiment in most undergraduate microbiology labs, the Bacterial Unknown Project, for online delivery using the university Learning Management System. We maintained the learning objectives suited for online delivery, such as creating an experimental design for identifying an unknown bacterium and communicating scientific results, while replacing or modifying those which could not be replicated, such as demonstration of sterile techniques, with learning objectives that highlighted skills of collaboration, peer evaluation, and scientific communication. Assessment of these new and modified learning objectives demonstrated positive student learning. Additionally, an anonymous postproject survey asked students whether they perceived the online Bacterial Unknown Project had increased their skill level in the areas highlighted by the revised learning objectives. Results reflected that 80% of the students reported the Unknown Project had increased their skills in all areas evaluated.
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http://dx.doi.org/10.1128/jmbe.v22i1.2415 | DOI Listing |
Palliat Support Care
January 2025
Department of Pediatrics, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
Objectives: Explore humanitarian healthcare professionals' (HCPs) perceptions about implementing children's palliative care and to identify their educational needs and challenges, including learning topics, training methods, and barriers to education.
Methods: Humanitarian HCPs were interviewed about perspectives on children's palliative care and preferences and needs for training. Interviews were transcribed, coded, and arranged into overarching themes.
JMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFJ Clin Orthop Trauma
March 2025
Department of Orthopaedic Surgery, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore, 308433, Singapore.
Objective: To evaluate the utility of three-dimensional (3D) anatomical models as an educational tool among Orthopaedic surgical trainees.
Methods: Seven types of 3D anatomical models - humerus, elbow, ankle, calcaneum, knee, femur, and pelvis- based on patients' computational tomography (CT) scans were printed in the study institution and used by surgical trainees preoperatively. Responses were collected in the form of a Likert scale questionnaire.
Imaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
View Article and Find Full Text PDFAm J Neurodegener Dis
December 2024
School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran.
Unlabelled: This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.
Objectives: To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.
Methods: A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.
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