The PROSPER model uses a three-tiered community partnership, university researcher, and Cooperative Extension-based technical assistance system to support the delivery of evidence-based interventions in communities. This study examines the trajectory and predictors of the collaborative relationship between technical assistance providers and community teams across the three phases of organization, implementation, and sustainability. Members of 14 PROmoting School-university-community Partnerships to Enhance Resilience (PROSPER) community teams and directors of local agencies rated communities' levels of readiness and adolescent substance use norms. Technical assistance providers rated their collaborative relationship with their teams at 14 occasions across 4.5 years. Results from mixed models show that levels of collaboration were stable until the sustainability phase, when they increased significantly. Team differences in change were significant during the implementation phase. Community readiness predicted levels of the collaborative relationship over time: high community readiness was associated with a high level of collaboration during organization, but a decline in collaboration during implementation. These results provide a more nuanced understanding of the relationship between technical assistance provision and community prevention teams and lead to recommendations to improve dissemination models to achieve a greater public health impact.
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http://dx.doi.org/10.1007/s11121-017-0812-2 | DOI Listing |
Sci Rep
December 2024
Department of Electronic Engineering, Yeungnam University, Gyeongsan, 38541, South Korea.
Natural honey is enriched with essential and beneficial nutrients. This study aimed to investigate the melliferous flora microscopic techniques and assess the biochemical properties of honey. Flavonoid and phenolic contents in honey samples were analyzed via colorimetric and Folin-Ciocalteu methods and the alpha-amylase, reducing power, and minerals using Pull's and spectroscopy methods.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
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December 2024
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
The Epstein-Barr virus (EBV) is widespread and has been related to a variety of malignancies as well as infectious mononucleosis. Despite the lack of a vaccination, antiviral medications offer some therapy alternatives. The EBV BZLF1 gene significantly impacts viral replication and infection severity.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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