Background: The relationship between work and health is complex and bidirectional, where work can have both health-harming and health-enhancing effects. Though employment is recognized as a social determinant of health, and clinical healthcare delivery systems are increasingly using screening tools to ask patients about social needs, little research has explored the extent to which employment-related social risk is captured in these screening tools. This study aimed to identify and characterize employment- and work-related questions in social risk screening tools that have been implemented in clinical healthcare delivery systems.
Methods: We conducted a qualitative content analysis of employment-related items in screening tools that have been implemented in clinical healthcare service delivery systems. Three content areas guided data extraction and analysis: Setting, Domain, and Level of Contextualization.
Results: Screening tools that asked employment-related questions were implemented in settings that were diverse in the populations served and the scope of care provided. The intent of employment-related items focused on four domains: Social Risk Factor, Social Need, Employment Exposure, and Legal Need. Most questions were found to have a low Level of Contextualization and were largely focused on identifying an individual's employment status.
Conclusions: Several existing screening tools include measures of employment-related social risk, but these items do not have a clear purpose and range widely depending on the setting in which they are implemented. In order to maximize the utility of these tools, clinical healthcare delivery systems should carefully consider what domain(s) they aim to capture and how they anticipate using the screening tools to address social determinants of health.
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http://dx.doi.org/10.1186/s12913-024-10976-3 | DOI Listing |
JMIR Form Res
March 2025
Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Background: Screening for cognitive impairment in primary care is important, yet primary care physicians (PCPs) report conducting routine cognitive assessments for less than half of patients older than 60 years of age. Linus Health's Core Cognitive Evaluation (CCE), a tablet-based digital cognitive assessment, has been used for the detection of cognitive impairment, but its application in primary care is not yet studied.
Objective: This study aimed to explore the integration of CCE implementation in a primary care setting.
PLoS One
March 2025
ICMR-Vector Control Research Centre, Department of Health Research, Ministry of Health and Family Welfare, Government of India, Medical Complex, Indira Nagar, Puducherry, India.
Malaria control in highly endemic regions relies heavily on vector control tools, particularly LLINs. The effectiveness of LLINs varies by eco-epidemiological conditions and brands. A comprehensive review of WHO interim-approved LLIN brands is necessary to address this variability.
View Article and Find Full Text PDFACS Appl Mater Interfaces
March 2025
Terahertz Research Center, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Single-bacterium diagnostic methods with unprecedented precision and rapid turnaround times are promising tools for facilitating the transition from empirical treatment to personalized anti-infection treatment. Terahertz (THz) radiation, a cutting-edge technology for identifying pathogens, enables the label-free and non-destructive detection of intermolecular vibrational modes and bacterial dielectric properties. However, this individual dielectric property-based detection and the mismatched spatial resolution are limited for the single-bacterium identification of various species of pathogens.
View Article and Find Full Text PDFJ Med Internet Res
March 2025
Inverness College, University of the Highlands and Islands, Inverness, GB.
Background: Artificial intelligence (AI) is rapidly transforming healthcare, offering significant advancements in patient care, clinical workflows, and nursing education. While AI has the potential to enhance health outcomes and operational efficiency, its integration into nursing practice and education raises critical ethical, social, and educational challenges that must be addressed to ensure responsible and equitable adoption.
Objective: This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing.
Gigascience
January 2025
Concordia University, Department of Computer Science and Software Engineering, 1455 Blvd. De Maisonneuve Ouest, Montreal, Quebec H3G 1M8, Canada.
Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers.
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