Objective: The ability to recall relevant medical knowledge within clinical contexts is a critical aspect of effective and efficient patient diagnosis and management. The ever-growing and changing body of medical literature requires learners to develop effective life-long learning techniques. Learners can more successfully build their fund of knowledge and ability to retrieve it by using evidence-based learning strategies. Our objective was to evaluate the study habits of internal medicine (IM) residents at an academic institution to understand if they apply key learning strategies for the American Board of Internal Medicine (ABIM) exam preparation. We also briefly review various learning strategies that can be applied to IM residency curricula.
Methods: A web-based survey consisting of 16 multiple-response questions on study habits was filled out by the IM residents in 2019 at Tufts Medical Center.
Results: Of the 75 residents invited to participate in the study, 69 responded (response rate = 92%). Of the responders, n=25 (36.2%) were post-graduate year (PGY)-1, n=20 (29.0%) were PGY-2, and n=24 (34.8%) were PGY-3 residents. More than half the residents (n=40, 58%) had Step 2 Clinical Knowledge (CK) scores > 250. Residents self-reported applying spaced learning (67%), interleaving (64%), retrieval (64%), and elaboration practices (46%) for exam preparation. There was a significant association between the Step 2 CK score and elaboration (p=0.017) technique but not with spaced learning, interleaving, or retrieval. The majority of residents felt not at all prepared (n=42, 60.9%) for the ABIM exam.
Conclusions: Despite two years of clinical training, 33% of the third-year residents felt inadequately prepared for the board certification exam. Incorporating evidence-based learning strategies into their daily curriculum may help them better prepare for the ABIM exam.
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http://dx.doi.org/10.7759/cureus.50052 | DOI Listing |
Eur J Dent Educ
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QU Health College of Dental Medicine, Qatar University, Doha, Qatar.
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Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
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January 2025
Department of Breast Surgery, Thyroid Surgery, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, No.141, Tianjin Road, Huangshi, 435000, Hubei, China.
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In cybersecurity, anomaly detection in tabular data is essential for ensuring information security. While traditional machine learning and deep learning methods have shown some success, they continue to face significant challenges in terms of generalization. To address these limitations, this paper presents an innovative method for tabular data anomaly detection based on large language models, called "Tabular Anomaly Detection via Guided Prompts" (TAD-GP).
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