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GMS J Med Educ ; 37(7): Doc91, 2020.
Article in English | MEDLINE | ID: covidwho-972237

ABSTRACT

Background: Diagnostic tests and examinations inform clinical decision making. Thus, an essential part of medical students' workplace-based training is dedicated to core skills in clinical diagnostic sciences. Due to a reduction of clinical internships for fifth-year students in the wake of COVID-19 learning activities replacing this aspect of training were needed. Project description: Virtual Patient online learning materials addressing clinical diagnostic sciences, specifically, radiology, were developed to prepare students for the transition to workplace-based learning. Three types of activities related to interprofessional patient treatment, showing how radiology knowledge improves the diagnosing and treatment of patients, were used to design the narrative of each virtual patient. The materials also showed students "how to learn" in the clinical workplace while showing "what to learn". Students complete relevant tasks and compare their approach with experts' approach in a self-directed way. Results: Twenty self-study quizzes, accompanied by nine interactive Webinars were developed, providing 13% of the overall available replacement learning materials for the summer term 2020. In June 2020, 486 students completed the program and collected a mean share of 16% (SD=10) of their required credits by choosing to learn with these materials. Conclusion: Developing virtual patients based on three types of clinical activities to prepare students for the transition to workplace based learning proved successful and allowed rapid development of learning materials. The presented online quiz format and webinar format showed high acceptance and interest among students.


Subject(s)
COVID-19/epidemiology , Computer-Assisted Instruction/methods , Education, Distance/organization & administration , Education, Medical, Undergraduate/organization & administration , Radiology/education , Educational Measurement , Humans , Pandemics , Patient Simulation , SARS-CoV-2
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