Objectives: Knowledge on clinical epidemiology is crucial to practice evidence-based medicine. We describe the development and validation of the Utrecht questionnaire on knowledge on Clinical epidemiology for Evidence-based Practice (U-CEP); an assessment tool to be used in the training of clinicians.
Study Design And Setting: The U-CEP was developed in two formats: two sets of 25 questions and a combined set of 50. The validation was performed among postgraduate general practice (GP) trainees, hospital trainees, GP supervisors, and experts. Internal consistency, internal reliability (item-total correlation), item discrimination index, item difficulty, content validity, construct validity, responsiveness, test-retest reliability, and feasibility were assessed. The questionnaire was externally validated.
Results: Internal consistency was good with a Cronbach alpha of 0.8. The median item-total correlation and mean item discrimination index were satisfactory. Both sets were perceived as relevant to clinical practice. Construct validity was good. Both sets were responsive but failed on test-retest reliability. One set took 24 minutes and the other 33 minutes to complete, on average. External GP trainees had comparable results.
Conclusion: The U-CEP is a valid questionnaire to assess knowledge on clinical epidemiology, which is a prerequisite for practicing evidence-based medicine in daily clinical practice.
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http://dx.doi.org/10.1016/j.jclinepi.2016.08.009 | DOI Listing |
Interact J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
J Glob Health
January 2025
Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Background: Psychological distress, such as depression and anxiety, impacts cardiovascular disease (CVD) prognosis and management. Illness comprehension is essential for effective treatment, but biases can lead to suboptimal outcomes. We explored psycho-cardiovascular disease (PCD) patient characteristics, with a specific focus on comprehension biases and treatment choices from patients' perspectives in China, to improve management strategies.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Center of Translational Oral Research (TOR), Department of Clinical Dentistry, University of Bergen, Bergen 5009, Norway.
Wood-based nanocellulose is emerging as a promising nanomaterial in the field of tissue engineering due to its unique properties and versatile applications. Previously, we used TEMPO-mediated oxidation (TO) and carboxymethylation (CM) as chemical pretreatments prior to mechanical fibrillation of wood-based cellulose nanofibrils (CNFs) to produce scaffolds with different surface chemistries. The aim of the current study was to evaluate the effects of these chemical pretreatments on serum protein adsorption on 2D and 3D configurations of TO-CNF and CM-CNF and then to investigate their effects on cell adhesion, spreading, inflammatory mediator production , and the development of foreign body reaction (FBR) .
View Article and Find Full Text PDFPLoS One
January 2025
Health Promotion Sciences Department, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, United States of America.
The complex healthcare system in the United States (US) poses significant challenges for people, particularly minorities such as refugees. Refugees often encounter additional layers of challenges to healthcare navigation due to unfamiliarity with the system, limited health literacy, and language barriers. Despite their challenges, it is difficult to identify the gaps as few tools exist to measure navigation competency among this population and many conventional tools assume English proficiency, making them inadequate for refugees and other immigrants.
View Article and Find Full Text PDFPLoS One
January 2025
School of Industrial and Management Engineering, Korea University, Seongbuk-gu, Seoul, Republic of Korea.
A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length.
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