Purpose: The Professional Graduate Medical School (PGMS) was established in 2003 in South Korea to train doctors that had better humanities and various educational backgrounds. By comparing the learning styles between students of the Medical College (MC) and PGMS, we investigated the characteristics of these students.

Methods: The Kolb Learning Style Inventory (LSI) is used to determine learning preferences. It is composed of 12 statements on concrete experience, reflective observation, abstract conceptualization, and active experimentation. Six hundred nine students from all years of the 2 medical schools completed the Kolb LSI between June 1st and June 30th, 2008 (response rate: 91.4%).

Results: MC students preferred Kolb's 'assimilator (56.3%)' and 'diverger (25.6%)', and PGMS students preferred Kolb's 'assimilator (61.2%)' and 'converger (19.3%)'. PGMS students showed a higher preference for abstract conceptualization compared with MC students (adjusted Odds Ratio=2.191; 95% Confidence Interval=1.115~4.306).

Conclusion: This study showed that the learning styles of PGMS and MC students differed. We can use this result not only in developing curricula and teaching strategies, but also in providing support to students.

Download full-text PDF

Source
http://dx.doi.org/10.3946/kjme.2009.21.2.125DOI Listing

Publication Analysis

Top Keywords

learning styles
12
pgms students
12
students
10
medical college
8
professional graduate
8
graduate medical
8
medical school
8
abstract conceptualization
8
students preferred
8
preferred kolb's
8

Similar Publications

Purpose: Free Open Access Medical Education (FOAMed) is an emergent phenomenon within medical education. The rise of FOAMed resources has meant that medical education needs no longer be confined to the lecture theatre or the hospital setting, but rather, can be produced and shared amongst any individual or group with access to internet and a suitable device. This study presents a review of the use of FOAMed resources by students as part of their university medical education.

View Article and Find Full Text PDF

Background: Neurocognitive health is influenced by multiple modifiable and non-modifiable lifestyle factors. Machine learning tools offer a promising approach to better understand complex models of cognitive function. We used extreme gradient boosting (XG Boost), an algorithm of decision-tree modeling, to analyze the association between 15 late-life lifestyle and demographic factors with episodic memory performance.

View Article and Find Full Text PDF

Clinical Manifestations.

Alzheimers Dement

December 2024

Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka, India.

Background: In the early stages of typical Alzheimer's disease, there is a well-documented pattern of memory deficits, especially episodic memory, substantiated with evidence of medial temporal lobe atrophy, specifically of the hippocampus in line with the memory deficits. Studies have shown that several other demographic, biological, and lifestyle factors influence memory and there is a need for identifying early risk factors and for the development of clinical intervention programs to delay or prevent cognitive decline. Therefore, the objective of this study is to explore the impact of various factors on episodic memory decline in an urban cohort.

View Article and Find Full Text PDF

Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).

Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.

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!