Some of the 'best practice' approaches to ensuring reproducibility of research can be difficult to implement in the developmental and clinical domains, where sample sizes and session lengths are constrained by the practicalities of recruitment and testing. For this reason, an important area of improvement to target is the reliability of measurement. Here we demonstrate that best-worst scaling (BWS) provides a superior alternative to Likert ratings for measuring children's subjective impressions. Seventy-three children aged 5-6 years rated the trustworthiness of faces using either Likert ratings or BWS over two sessions. Individual children's ratings in the BWS condition were significantly more consistent from session 1 to session 2 than those in the Likert condition, a finding we also replicate with a large adult sample (N = 72). BWS also produced more reliable ratings at the group level than Likert ratings in the child sample. These findings indicate that BWS is a developmentally appropriate response format that can deliver substantial improvements in reliability of measurement, which can increase our confidence in the robustness of findings with children.
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http://dx.doi.org/10.3758/s13428-021-01566-w | DOI Listing |
Background: The COVID-19 pandemic exposed weaknesses in healthcare systems and disparities in healthcare access across sub-Saharan Africa (SSA). The insights of frontline healthcare professionals (HCPs), and healthcare researchers involved with the response to COVID in SSA are crucial to ensuring that health systems are optimally prepared for the next pandemic threat. Nonetheless, there is limited consensus as to what are the clinical and public health research priorities necessary to ensure that SSA is optimally prepared and responsive to future pandemics.
View Article and Find Full Text PDFHealth Promot Chronic Dis Prev Can
January 2025
School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Introduction: Strategic knowledge mobilization efforts are needed to enhance uptake and use of the Canadian 24-Hour Movement Guidelines (24HMG), which describe optimal amounts of physical activity, sedentary behaviour and sleep each day for overall health. The Whole Day Matters Toolkit for Primary Care is an evidence-informed resource to help primary care providers (PCPs) disseminate the 24HMGs. The purpose of this study was to describe gaining consensus on toolkit components through iterative revisions to improve its utility in preparation for the September 2022 launch, and to summarize early dissemination efforts.
View Article and Find Full Text PDFAdv Orthop
January 2025
Orlando Health Jewett Orthopedic Institute, Orlando, Florida, USA.
Advances in artificial intelligence (AI), machine learning, and publicly accessible language model tools such as ChatGPT-3.5 continue to shape the landscape of modern medicine and patient education. ChatGPT's open access (OA), instant, human-sounding interface capable of carrying discussion on myriad topics makes it a potentially useful resource for patients seeking medical advice.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
Background: This mixed methods study identified needed refinements to a telehealth-delivered cultural and linguistic adaptation of Meaning-Centered Psychotherapy for Chinese patients with advanced cancer (MCP-Ch) to enhance acceptability, comprehensibility, and implementation of the intervention in usual care settings, guided by the Ecological Validity Model (EVM) and the Practical, Robust Implementation and Sustainability Model (PRISM).
Methods: Fifteen purposively sampled mental health professionals who work with Chinese cancer patients completed surveys providing Likert-scale ratings on acceptability and comprehensibility of MCP-Ch content (guided by the EVM) and pre-implementation factors (guided by PRISM), followed by semi-structured interviews. Survey data were descriptively summarized and linked to qualitative interview data.
JMIR Med Inform
January 2025
Department of Medical Ultrasound, West China Hospital of Sichuan University, 37 Guoxue Alley, Chengdu, 610041, China, 86 18980605569.
Background: Artificial intelligence chatbots are being increasingly used for medical inquiries, particularly in the field of ultrasound medicine. However, their performance varies and is influenced by factors such as language, question type, and topic.
Objective: This study aimed to evaluate the performance of ChatGPT and ERNIE Bot in answering ultrasound-related medical examination questions, providing insights for users and developers.
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