Background: The modalities of learning could be Visual, Auditory, Reading/ writing, and Kinesthetic (VARK). VARK concept has been used to know the learning preferences among First Year Medical Students. But learning preferences for "Research Methodology" have rarely been reported.
Objective: This study was conducted to know the learning preferences for "Research Methodology" using VARK concept.
Methods: A questionnaire-based study was conducted among medical undergraduate students who had completed their student research project during their 4th semester. The questionnaire was constructed using VARK concept to know: 1. Learning preference of research methodology, 2. Characteristics of learning preference of participants are classified under "Visual(V), Auditory(A), Read/write(R), and Kinaesthetic(K). The students were approached in the classrooms, and the data was collected and was analysed using SPSS version 11.5. Results were expressed as proportions in appropriate tables and graphs.
Results: Totally 157 students were participated in the study. Most (1164/1570 [74.1%]) of them expressed Unimodal learning preference as compared to Multimodal learning (84/1570 [5.4%]). Majority preferred Auditory (641/1560 [40.8%]) followed by Visual (542/1570 [34.5%]) for learning "Research Methodology". This study showed that gender of the students did not influence learning preference. However, the total number of preference for "Kinaesthetic" in those who had additional research experience through ICMR-STS and workshop on research methodology are more compared to those who didn't.
Conclusion: Unimodal with Auditory followed by Visual mode was preferred for learning "Research Methodology".
Download full-text PDF |
Source |
---|
BMC Med Inform Decis Mak
January 2025
Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
View Article and Find Full Text PDFNat Med
January 2025
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records.
View Article and Find Full Text PDFNat Neurosci
January 2025
Brain Research Institute, University of Zurich, Zurich, Switzerland.
Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Pasto, 520001, Colombia.
This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions.
View Article and Find Full Text PDFBMJ Open
January 2025
Universidade Federal de Pelotas, Pelotas, RS, Brazil.
Introduction: With the development of technology, the use of machine learning (ML), a branch of computer science that aims to transform computers into decision-making agents through algorithms, has grown exponentially. This protocol arises from the need to explore the best practices for applying ML in the communication and management of occupational risks for healthcare workers.
Methods And Analysis: This scoping review protocol details a search to be conducted in the academic databases, Public Medical Literature Analysis and Retrieval System Online, through the Virtual Health Library: Medical Literature Analysis and Retrieval System, Latin American and Caribbean Literature in Health Sciences, West Pacific Region Index Medicus, Nursing Database and Scientific Electronic Library Online, Scopus, Web of Science and IEEE Xplore Digital Library and Excerpta Medica Database.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!