Purpose: Job satisfaction plays a large role in enhancing retention and minimizing loss of physicians from careers in academic medicine. The authors explored the effect of learning communities (LCs) on the faculty members' job satisfaction.
Methods: Between October 2011 and May 2012, the authors surveyed 150 academic clinical faculty members serving as LC mentors for students at five US medical schools. Factor analysis was used to explore satisfaction themes and relationships between these themes and other characteristics.
Results: Factor analysis revealed two major sources of this satisfaction: a Campus Engagement factor (e.g., feeling happier, improved sense of community, better communication skills, and feeling more productive) and a skills factor (e.g., improved clinical skills, being a better doctor). Higher Campus Engagement factor satisfaction was associated with less desire to leave the learning community (p = 0.01) and more FTE support for role in LC (p = 0.01). Higher skills factor satisfaction was associated with the school that provided more structured faculty development (p = 0.0001).
Conclusion: Academic clinical faculty members reported serving as a mentor in an LC was a strong source of job satisfaction. LC may be a tool for retaining clinical faculty members in academic careers.
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http://dx.doi.org/10.3109/0142159X.2014.947940 | DOI Listing |
Sci Rep
December 2024
Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
In order to plan and facilitate the culture of personalized / precision medicine in medical practices within any healthcare institution, it is requisite for healthcare professionals like clinicians to have a clear understanding and approach towards the practices of personalized genetic testing. This nationwide cross-sectional study aimed to measure the perceptions and knowledge of clinicians towards personalized genetic testing and assess their current practices of personalized genetic testing in clinical settings through an online self-administered questionnaire in Saudi Arabia. The results of the study revealed that almost two-fifths of participants were responsible for ordering genetic tests directly (39.
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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December 2024
Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093, Lublin, Poland.
Using Fourier Transform Infrared spectroscopy (FTIR), it is possible to show chemical composition of materials and / or profile chemical changes occurring in tissues, cells, and body fluids during onset and progression of diseases. For diagnostic application, the use of blood would be the most appropriate in biospectroscopy studies since, (i) it is easily accessible and, (ii) enables frequent analyses of biochemical changes occurring in pathological states. At present, different studies have investigated potential of serum, plasma and sputum being alternative biofluids for lung cancer detection using FTIR.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
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