Association of learning styles with research self-efficacy: study of short-term research training program for medical students.

Clin Transl Sci

Department of Psychiatry, Sam and Rose Stein Institute for Research on Aging, and Clinical and Translational Research Institute, University of California, San Diego, California, USA.

Published: December 2014

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4268258PMC
http://dx.doi.org/10.1111/cts.12197DOI Listing

Publication Analysis

Top Keywords

learning styles
24
medical students
16
training programs
12
short-term training
8
students' gender
8
gender ranking
8
ranking medical
8
self-efficacy baseline
8
training
7
medical
7

Similar Publications

Adapting a style based generative adversarial network to create images depicting cleft lip deformity.

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 PDF

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 PDF

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 PDF

The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.

BMC 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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!