Clinical history taking and physical examination are two of the most important competencies of physicians. In addition to informing diagnoses, these activities build rapport and establish relationships between caregivers and patients. Despite this, emphasis on the assessment of bedside clinical skills is declining. To prepare our students for clinical work, we began a clinical competency, personalised teaching programme in which students perform a history and physical examination in front of a master clinical teacher (MCT) approximately every 2 weeks throughout their core clerkship year. The MCT works with the student in a clinical encounter, providing personalised bedside instruction on all features of being a clinician including bedside manner, history-taking skills, physical examination skills, and clinical reasoning. The MCT then provides an assessment of student's competency development and gives feedback to the student about what they do well and where they have opportunities for growth. Assessment data are collected and tracked longitudinally across the clerkship phase to ensure that each student is progressing developmentally. With over 6000 observations of student performance, we are able to discern competency development and growth over time. We can identify if a student is not improving as expected during their clerkship phase and intervene by providing extra practice and training. This core clerkship teaching programme has been well received by both students and instructors and has led us to pilot this approach during the post-clerkship phase of our medical training.
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http://dx.doi.org/10.1111/tct.13562 | DOI Listing |
Sensors (Basel)
December 2024
School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson's disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 ( = 11) was collected in a previous study.
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December 2024
Faculty of Medicine, Department of Kinesiology, Université Laval, Quebec City, QC G1V OA6, Canada.
Foot strike patterns influence vertical loading rates during running. Running retraining interventions often include switching to a new foot strike pattern. Sudden changes in the foot strike pattern may be uncomfortable and may lead to higher step-to-step variability.
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December 2024
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404, Taiwan.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication. While many studies suggest that individuals with ASD struggle with emotion processing, the association between emotion processing and autistic traits in non-clinical populations is still unclear. We examine whether neurotypical adults' facial emotion recognition and expression imitation are associated with autistic traits.
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December 2024
Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
In the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters.
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December 2024
School of Mechanical Engineering and Automation, Foshan University, Foshan 528225, China.
Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces.
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