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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284922PMC
http://dx.doi.org/10.1002/lrh2.10328DOI Listing

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The obstetrics and gynecology (OB/GYN) clerkship is a uniquely enriching and challenging rotation for medical students. Available literature prepares students for medicine and surgery clerkships, but few guides identify and discuss the unique characteristics of OB/GYN that impact student learning and performance during the clerkship. Here, we aim to highlight the specific clinical learning environments, emotionally sensitive experiences, and technical performance expectations that students should anticipate and be prepared for in the OB/GYN clerkship.

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Background: Tabletop Disaster Exercise (TDE) is a unique learning method through simulation designed to improve disaster preparedness. It is used every year to train health workers and students in disaster preparedness. However, no review has summarized the potential of TDE.

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Background: Cognitive integration occurs when trainees make conceptual connections between relevant knowledges and is known to improve learning. While several experimental studies have demonstrated how text and audio-visual instruction can be designed to enhance cognitive integration, clinical skills training in real-world contexts may require alternative educational strategies. Introducing three-dimensional (3D) printed models during clinical skills instruction may offer unique learning opportunities to support cognitive integration.

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Background: Three-dimensional (3D) visualization has become increasingly prevalent in orthopedic education to tackle the distinct anatomical challenges of the field. However, there is a conspicuous lack of systematic reviews that thoroughly evaluate both the advantages and drawbacks of integrating 3D with problem-based learning (3D + PBL).

Methods: A rigorous search of English databases (Cochrane Library, Embase, PubMed, Scopus, and Web of Science) and Chinese databases (National Knowledge Infrastructure: CNKI, Chongqing VIP: VIP, and Wan Fang) were performed up to July 2024 to identify relevant studies.

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Purpose: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.

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