Purpose: With a growing need for developing future physician scientists, identifying characteristics of medical students who are likely to benefit from research training programs is important. This study assessed if specific learning styles of medical students, participating in federally funded short-term research training programs, were associated with research self-efficacy, a potential predictor of research career success.
Method: Seventy-five first-year medical students from 28 medical schools, selected to participate in two competitive NIH-supported summer programs for research training in aging, completed rating scales to evaluate learning styles at baseline, and research self-efficacy before and after training. We examined associations of individual learning styles (visual-verbal, sequential-global, sensing-intuitive, and active-reflective) with students' gender, ranking of medical school, and research self-efficacy.
Results: Research self-efficacy improved significantly following the training programs. Students with a verbal learning style reported significantly greater research self-efficacy at baseline, while visual, sequential, and intuitive learners demonstrated significantly greater increases in research self-efficacy from baseline to posttraining. No significant relationships were found between learning styles and students' gender or ranking of their medical school.
Conclusions: Assessments of learning styles may provide useful information to guide future training endeavors aimed at developing the next generation of physician-scientists.
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http://dx.doi.org/10.1111/cts.12197 | DOI Listing |
Sci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Background: The National Commission for Academic Accreditation and Assessment (NCAAA) in Saudi Arabia underscores the importance of assessing student satisfaction to ensure program quality. No previous studies have explored the satisfaction levels of dental students enrolled in clinical Periodontics courses at King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS). This study aims to assess dental students' satisfaction with clinical Periodontics courses and to explore potential differences in satisfaction based on gender and academic level.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Health Management of Public Health, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou, 450001, Henan, China.
Background: Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the effectiveness of machine learning-based lifestyle factors in predicting cardiovascular and all-cause mortality and compare the results obtained by traditional methods.
Method: A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010.
Behav Sci (Basel)
January 2025
Department of Economics and Business, Faculty of Business and Communication Studies, University of Vic-Central University of Catalonia, 08500 Vic, Spain.
Ethical management is key to ensuring organizational sustainability, through resources such as autonomy or self-efficacy. However, economic and social uncertainty occasionally leads to adaptive responses that prioritize profit as the primary interest, blurring the integrating role of ethical leadership. There are a number of studies that support this reality in a virtual work environment.
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