Background: Nutrition education has presented an ongoing challenge to medical educators. In the 2007-2008 academic year, Harvard Medical School replaced its dedicated Preventive Medicine and Nutrition course with an integrated curriculum. The objective of the current study was to assess the effect of the curriculum change on medical student attitudes and knowledge about nutrition.
Methods: A survey was administered in a quasi-experimental design to students in the last class of the dedicated curriculum (n = 131) and the first class of the integrated curriculum (n = 135) two years after each class completed the required nutrition course. Main measures were attitude scores based on modified Nutrition in Patient care Survey and satisfaction ratings, performance on a nutrition knowledge test, and demographic variables. Two-tailed t-tests were performed.
Results: Response rates were 50.4% and 42.2%. There were no differences between the groups in attitude scores from the Nutrition in Patient care Survey (p = 0.43) or knowledge scores (p = 0.63). Students with the integrated curriculum were less satisfied with both the quantity (p < 0.0001) and quality (p = 0.008) of their nutrition education, and were more likely to have completed optional online nutrition training modules (p = 0.0089).
Conclusions: Medical student attitudes and knowledge about nutrition were not affected by the model of nutrition education they receive, though students in an integrated curriculum may feel their education is inadequate and seek additional training.
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http://dx.doi.org/10.1186/1472-6920-11-58 | DOI Listing |
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Department of Medical Oncology, Hospital Clinico Universitario, INCLIVA, Biomedical Research Institute, University of Valencia, Avenida Menendez Pelayo nro 4 accesorio, Valencia, Spain.
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January 2025
School of Electrical Engineering, Nanjing Vocational University of Industry Technology, Nanjing, 210023, China.
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Department of Pharmacology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, 710061, China.
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January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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