The Pearson correlation coefficient can be translated to a common language effect size, which shows the probability of obtaining a certain value on one variable, given the value on the other variable. This common language effect size makes the size of a correlation coefficient understandable to laypeople. Three examples are provided to demonstrate the application of the common language effect size in interpreting Pearson correlation coefficients and multiple correlation coefficients.
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http://dx.doi.org/10.1080/00221309.2019.1585321 | DOI Listing |
BMC Womens Health
January 2025
School of Nursing, Fudan University, 305 Fenglin Road, Shanghai, 200032, China.
Purpose: This scoping review aims to summarize online health information seeking (OHIS) behavior among breast cancer patients and survivors, identify research gaps, and offer insights for future studies.
Methods: Following Arksey and O'Malley's framework, we conducted a review across PubMed, Web of Science, CINAHL, MEDLINE, Cochrane, Embase, CNKI, Wanfang Data, and SinoMed, covering literature from 1 January 2014 to 13 August 2023. A total of 1,368 articles were identified, with 33 meeting the inclusion criteria.
BMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
University Clinic for Interdisciplinary Orthopedic Pathways (UCOP), Elective Surgery Center, Silkeborg Regional Hospital, Silkeborg, Denmark.
Background: Access to clear and comprehensible health information is crucial for patient empowerment, leading to improved self-care, adherence to treatment plans, and overall health outcomes. Traditional methods of information delivery, such as written documents and oral communication, often result in poor memorization and comprehension. Recent innovations, such as animation videos, have shown promise in enhancing patient understanding, but comprehensive investigations into their effectiveness across various health care settings are lacking.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
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