Objective: The Tuscan Emergency Medicine Initiative is an international collaboration designed to create a sustainable emergency medicine training and qualification process in Tuscany, Italy. Part of the program involves training all emergency physicians currently practicing in the region. This qualification process includes didactic lectures, clinical rotations and practical workshops for those with significant emergency department experience. Lectures in the didactic portion were given by both emergency medicine (EM) and non-EM faculty. We hypothesized that faculty who worked clinically in EM would give more effective lectures than non-EM faculty.
Methods: Fifty-one emergency physicians from the hospitals surrounding Florence completed the course, which included 48 one-hour lectures. Twenty lectures were given by practicing emergency physicians and 28 were given by non-EM faculty. Participants completed an evaluation at the end of each session using a 5-point Likert scale describing the pertinence of the lecture to EM, the efficacy and clarity of the presentation, the accuracy of the information and the didactic ability of the lecturer.
Results: A mean of 38.5 evaluations was completed for each lecture. Every factor was significantly higher for lectures given by EM faculty: the pertinence of the lecture to EM (4.46 vs. 4.16, p < 0.001), the efficacy of the faculty (4.10 vs. 3.91, p < 0.001), the accuracy of the lecture content (4.16 vs 3.96, p < 0.001), and the didactic ability of the instructors (4.02 vs. 3.85, p = 0.001).
Conclusions: When teaching EM, evaluations of lectures in this training intervention were higher for lectures given by EM faculty than by non-EM faculty.
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Implement Sci Commun
January 2025
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School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge.
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