Purpose: To develop a Chinese version of the new, 5-level EQ-5D descriptive system (EQ-5D-5L) from the existing EQ-5D-3L by identifying Chinese label wording suitable for constructing EQ-5D-5L's 5-point response scales.
Methods: In face-to-face interviews, perceived severity of selected Chinese labels when they were used to describe EQ-5D health problems was measured from 50 native Chinese speakers using a 0 (no problems) to 100 (the worst problems) visual analog scale. Selection of label wording was based on the severity scores and semantic similarity with label wording used in the existing English and Spanish EQ-5D-5L.
Results: The severity scores supported the use of Chinese wording of 'only a little' (range of median: 12.5-17), 'moderate' (range of median: 50-53), and 'severe' (range of median: 82.5-90) as the descriptors for the intermediate functional levels of the five EQ-5D dimensions and the label wording of 'very severe' (median: 90) to describe the worst level of pain/discomfort and anxiety/depression.
Conclusions: The Chinese version of the EQ-5D-5L comprises descriptors with similar interpretations as those used its English and Spanish counterparts. The response scaling exercise is a useful method for cross-cultural adaptation of health-status instruments.
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http://dx.doi.org/10.1007/s11136-012-0200-0 | DOI Listing |
United States and European Union laws demand separate clinical studies in children as a condition for drugs' marketing approval. Justified by carefully framed pseudo-scientific wordings, more so the European Medicines Agency than the United States Food and Drug Administration, "Pediatric Drug Development" is probably the largest abuse in medical research in history. Preterm newborns are immature and vulnerable, but they grow.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Aga Khan University, Nairobi, Kenya.
Background: Population growth and an increase in the number of Africans who survive to old age puts them at a higher risk of developing neurodegenerative diseases such as dementia and Alzheimer's. Little research has been conducted on community knowledge and perceptions of dementia in rural settings in Kenya.
Method: Community health volunteers, healthcare workers (HCWs), chiefs and assistant chiefs (n = 35) participated in five focus group discussions, each comprising seven- eight people.
Front Psychol
December 2024
Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy.
Background: The present study investigated whether semantic processing of word and object primes can bias visual attention using top-down influences, even within an exogenous cueing framework. We hypothesized that real words and familiar objects would more effectively bias attentional engagement and target detection than pseudowords or pseudo-objects, as they can trigger prior knowledge to influence attention orienting and target detection.
Methods: To examine this, we conducted two web-based eye-tracking experiments that ensured participants maintained central fixation on the screen during remote data collection.
Ther Adv Musculoskelet Dis
December 2024
Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos, Madrid, Spain.
Background: Rheumatology has experienced notable changes in the last decades. New drugs, including biologic agents and Janus kinase (JAK) inhibitors, have blossomed. Concepts such as window of opportunity, arthralgia suspicious for progression, or difficult-to-treat rheumatoid arthritis (RA) have appeared; and new management approaches and strategies such as treat-to-target have become popular.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, United States.
Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.
Materials And Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.
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