Background: Presentation of risk information influences patients' ability to interpret health care options. Little is known about this relationship between risk presentation and interpretation among American Indians.
Methods: Three hundred American Indian employees on a western American Indian reservation were invited to complete an anonymous written survey. All surveys included a vignette presenting baseline risk information about a hypothetical cancer and possible benefits of 2 prevention plans. Risk interpretation was assessed by correct answers to 3 questions evaluating the risk reduction associated with the plans. Numeric information was the same in all surveys, but framing varied; half expressed prevention benefits in terms of relative risk reduction and half in terms of absolute risk reduction. All surveys used text to describe the benefits of the 2 plans, but half included a graphic image. Surveys were distributed randomly. Responses were analyzed using binary logistic regression with the robust variance estimator to account for clustering of outcomes within participant.
Results: Use of a graphic image was associated with higher odds of correctly answering 3 risk interpretation questions (odds ratio = 2.5, 95% confidence interval = 1.5-4.0, P < 0.001) compared to the text-only format. These findings were similar to those of previous studies carried out in the general population. Neither framing information as relative compared to absolute risk nor the interaction between graphic image and relative risk presentation was associated with risk interpretation.
Conclusion: One type of graphic image was associated with increased understanding of risk in a small sample of American Indian adults. The authors recommend further investigation of the effectiveness of other types of graphic displays for conveying health risk information to this population.
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http://dx.doi.org/10.1177/0272989X10391096 | DOI Listing |
Atheroscler Plus
March 2025
Cardiology Department, Prince Khaled Ben Sultan Cardiac Centre, Armed Forces Southern Region, Khamis Muchait, Saudi Arabia.
Graphical Abstract: Dyslipidemia Patterns in Patients with Acute Coronary Syndrome.Image 1.
View Article and Find Full Text PDFNeurocomputing (Amst)
January 2025
Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.
View Article and Find Full Text PDFNeurol Sci
January 2025
Epilepsy Center, Department of Neurology, West China Hospital of Sichuan University, Chengdu, China.
This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups.
View Article and Find Full Text PDFMaterials (Basel)
December 2024
School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
Fatigue failure poses a serious challenge for ensuring the operational safety of critical components subjected to cyclic/random loading. In this context, various machine learning (ML) models have been increasingly explored, due to their effectiveness in analyzing the relationship between fatigue life and multiple influencing factors. Nevertheless, existing ML models hinge heavily on numeric features as inputs, which encapsulate limited information on the fatigue failure process of interest.
View Article and Find Full Text PDFIndian J Psychiatry
November 2024
Department of Psychiatry and Behavioural Sciences, Kalinga Institute of Medical Sciences (KIMS), KIIT University, Bhubaneswar, Odisha, India.
Background: First responders are at high risk for adverse mental health outcomes following trauma exposure during disaster response. This mixed methods study aimed to quantify psychological impacts and explore personal experiences among first responders after the Bahanaga train accident that killed 294 passengers in the month of June 2023.
Methods: For qualitative data, in-depth interviews (IDIs) were conducted, and quantitative data was collected using the PTSD checklist for DSM-5 (PCL-5), generalized anxiety disorder scale (GAD-7), and patient health questionnaire (PHQ-9) for symptoms of post-traumatic stress disorder (PTSD), depression, and anxiety.
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