Objective: To assess factors associated with positive COVID-19 tests, perspectives on health-related care delivery during pandemic, and factors supporting resilience among members of the Navajo Nation.
Methods And Analysis: From May through October 2021, a multi-institutional team recruited participants (n=154) to complete a 49-item questionnaire or participate in focus group (n=14) about their experience with COVID-19 and the effects on their use and access to allopathic and traditional health care. A multi-investigator, phenomenological approach summarized focus group experiences.
Results: While 72% had been tested for COVID-19, only 27.5% reported a positive test. Positive tests were not associated with household size or multigenerational homes, though time to grocery store was (p=0.04). There were no significant differences in allopathic or traditional medical care experiences from before and during the pandemic. Despite limited internet access, 28.8% chose a telehealth appointment and 42% expressed satisfaction with their experience. Discussion themes revealed perceived disruptions of healthcare needs with acknowledgement that healthcare providers were supportive throughout the Navajo Nation quarantine.
Conclusion: Presence of co-morbidities and living in multigenerational homes do not explain the disproportionate effects of COVID-19 among American Indian communities. Strengthening family and community bonds supported resilience in these communities.
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http://dx.doi.org/10.1136/bmjph-2023-000061 | DOI Listing |
JMIR Res Protoc
January 2025
College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Background: There is limited evidence of high-quality, accessible, culturally safe, and effective digital health interventions for Indigenous mothers and babies. Like any other intervention, the feasibility and efficacy of digital health interventions depend on how well they are co-designed with Indigenous communities and their adaptability to intracultural diversity.
Objective: This study aims to adapt an existing co-designed mobile health (mHealth) intervention app with health professionals and Aboriginal and/or Torres Strait Islander mothers living in South Australia.
PLoS One
January 2025
Department of Preventive Medicine, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.
Background: Despite the rising prevalence of common mental symptoms, information is scarce on how health workers make sense of symptoms of mental disorders and perceive a link with inadequate water, sanitation, and hygiene (WASH) as work stressors to understand causation and produce useful knowledge for policy and professionals. Therefore, this study aimed to explore how health workers perceive the link between inadequate WASH and common mental symptoms (CMSs) at hospitals in central and southern Ethiopian regions.
Methods: We used an interpretive and descriptive phenomenological design guided by theoretical frameworks.
PLoS One
January 2025
Animal and Human Health Department, International Livestock Research Institute, Nairobi, Kenya.
Non-conformance with antibiotic withdrawal period guidelines represents a food safety concern, with potential for antibiotic toxicities and allergic reactions as well as selecting for antibiotic resistance. In the Kenyan domestic pig market, conformance with antibiotic withdrawal periods is not a requirement of government legislation and evidence suggests that antibiotic residues may frequently be above recommended limits. In this study, we sought to explore enablers of and barriers to conformance with antibiotic withdrawal periods for pig farms supplying a local independent abattoir in peri-urban Nairobi.
View Article and Find Full Text PDFPLoS One
January 2025
Medical Image Processing Research Group (MIPRG), Dept. of Elect. & Comp. Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. However, these methods have limitations in high spatial-resolution applications, often resulting in blurring or residual artifacts.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
BIFOLD─Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
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