The Utrecht questionnaire (U-CEP) measuring knowledge on clinical epidemiology proved to be valid.

J Clin Epidemiol

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CX Utrecht, The Netherlands.

Published: February 2017

AI Article Synopsis

  • The Utrecht questionnaire on Clinical Epidemiology for Evidence-based Practice (U-CEP) was developed to assess clinicians' knowledge necessary for evidence-based medicine.
  • The questionnaire consists of two formats: a set of 25 questions and a combined set of 50, which underwent extensive validation processes among various medical trainees and experts.
  • Results showed that the U-CEP has good internal consistency and validity, making it a reliable tool, although it faced challenges with test-retest reliability and varying completion times.

Article Abstract

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.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jclinepi.2016.08.009DOI Listing

Publication Analysis

Top Keywords

knowledge clinical
16
clinical epidemiology
16
utrecht questionnaire
8
evidence-based medicine
8
internal consistency
8
item-total correlation
8
correlation item
8
item discrimination
8
construct validity
8
test-retest reliability
8

Similar Publications

Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis.

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.

View Article and Find Full Text PDF

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 PDF

Effects of Chemical Pretreatments of Wood Cellulose Nanofibrils on Protein Adsorption and Biological Outcomes.

ACS 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 PDF

Developing and validating a HEalthCare NAvigation Competency (HECNAC) Scale for refugees in the United States.

PLoS 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 PDF

Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

PLoS 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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!