Background: Board certification is an important professional qualification and a prerequisite for credentialing, and the Accreditation Council for Graduate Medical Education (ACGME) assesses board certification rates as a component of residency program effectiveness. To date, research has shown that preresidency measures, including National Board of Medical Examiners scores, Alpha Omega Alpha Honor Medical Society membership, or medical school grades poorly predict postresidency board examination scores. However, learning styles and temperament have been identified as factors that 5 affect test-taking performance. The purpose of this study is to characterize the learning styles and temperaments of pediatric residents and to evaluate their relationships to yearly in-service and postresidency board examination scores.
Methods: This cross-sectional study analyzed the learning styles and temperaments of current and past pediatric residents by administration of 3 validated tools: the Kolb Learning Style Inventory, the Keirsey Temperament Sorter, and the Felder-Silverman Learning Style test. These results were compared with known, normative, general and medical population data and evaluated for correlation to in-service examination and postresidency board examination scores.
Results: The predominant learning style for pediatric residents was converging 44% (33 of 75 residents) and the predominant temperament was guardian 61% (34 of 56 residents). The learning style and temperament distribution of the residents was significantly different from published population data (P = .002 and .04, respectively). Learning styles, with one exception, were found to be unrelated to standardized test scores.
Conclusions: The predominant learning style and temperament of pediatric residents is significantly different than that of the populations of general and medical trainees. However, learning styles and temperament do not predict outcomes on standardized in-service and board examinations in pediatric residents.
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http://dx.doi.org/10.4300/JGME-D-10-00147.1 | DOI Listing |
BMC Med Educ
January 2025
University of Birmingham, Birmingham, United Kingdom.
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 PDFAlzheimers Dement
December 2024
University of California, San Diego, La Jolla, CA, USA.
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 PDFAlzheimers 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 PDFMayo Clin Proc Digit Health
December 2024
School of Computed and Augmented Intelligence, Arizona State University, Tempe, AZ.
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.
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