This article addresses data privacy issues as they relate to multisystem collaborations for prearrest deflection into treatment and services for those suffering from a substance use disorder. The authors explore how the US data privacy regulations pose barriers to collaboration and care coordination and how data privacy regulations affect researchers' ability to evaluate the impact of interventions intentioned to facilitate access to care. Fortunately, this regulatory landscape is evolving to strike a balance between protecting health information and sharing it for research, evaluation, and operations, including comments on the newly proposed federal administrative rule that will shape the future of deflection and health access in the United States.
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http://dx.doi.org/10.1016/j.jval.2023.05.008 | DOI Listing |
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFBMC Nurs
January 2025
The First Affiliated Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, China.
Background: Self-management is regarded as a crucial factor influencing the effectiveness of home-based cardiac rehabilitation for patients with coronary heart disease. In nursing practice, nurses employ a variety of strategies to enhance self-management of patients. However, there exists a disparity in nurses' perceptions and practical experiences with these strategies.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
BMC Nurs
January 2025
Department of Nursing Sciences, College of Applied Medical Sciences, Shaqra University, Shaqra, Saudi Arabia.
Background: College-aged students are at risk for experiencing negative events that may influence their future health and life. Those negative events or stressors may vary in type and severity. Stress and bullying are prevalent among nursing students that may affect their academic motivation.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, 81th Meishan Road, Hefei, 230032, Anhui Province, China.
Introduction: School-based universal depression screening (SBUDS) is an effective method for early identification of depression. As parents are the primary decision-makers for their children's acceptance of healthcare services, this study aims to examine rural and urban parental acceptance of SBUDS.
Methods: The study assessed parental acceptance of SBUDS for their children and its association with self-reported parental perception of depression (i.
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