A well-conceived evaluation framework increases understanding of a program's goals and objectives, facilitates the identification of outcomes and can be used as a planning tool during program development. Herein we describe the origins and development of an evaluation framework that recognises that implementation is influenced by the setting in which it takes place, the individuals involved and the processes by which implementation is accomplished. The framework includes an evaluation hierarchy that focuses on outcomes for consumers, providers and the care delivery system, and is structured according to six domains: program delivery, impact, sustainability, capacity building, generalisability and dissemination. These components of the evaluation framework fit into a matrix structure, and cells within the matrix are supported by relevant evaluation tools. The development of the framework has been influenced by feedback from various stakeholders, existing knowledge of the evaluators and the literature on health promotion and implementation science. Over the years, the framework has matured and is generic enough to be useful in a wide variety of circumstances, yet specific enough to focus data collection, data analysis and the presentation of findings.
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http://dx.doi.org/10.1071/AH15117 | DOI Listing |
Eur J Dent Educ
January 2025
QU Health College of Dental Medicine, Qatar University, Doha, Qatar.
Aims: This study aimed to evaluate the impact of community-based dental education (CBDE) on the learning experiences of undergraduate dental students and recent dental graduates from two diverse geographical regions.
Methods: The study followed a cross-sectional design, conducted online using Google Forms, with ethical approval from Qatar University. A non-probability purposive sampling method was used to recruit dental students and recent graduates from three institutions in India and one in Qatar.
Cardiovasc Eng Technol
January 2025
Department of Hydrodynamic Systems, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 1-3, Budapest, 1111, Hungary.
Purpose: The initiation of intracranial aneurysms has long been studied, mainly by the evaluation of the wall shear stress field. However, the debate about the emergence of hemodynamic stimuli still persists. This paper builds on our previous hypothesis that secondary flows play an important role in the formation cascade by examining the relationship between flow physics and vessel geometry.
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January 2025
College of Engineering, Ocean University of China, Qingdao, 266404, China.
Although deterministic analysis can provide initial insights into slope stability, there is no way to reflect the true distribution of soil properties within a slope. To further investigate the effects of the spatial variability of soil properties on the stability and failure mechanism of slope under different foundation types, this study develops a framework combining simple limit equilibrium method (LEM), Monte Carlo Simulation (MCS), and random field to incorporate these factors into slope probabilistic stability analysis. The slope models of two typical foundations (e.
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January 2025
Department of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
With the rapid advancement of proteomics, numerous scholars have investigated the intricate relationships between plasma proteins and various diseases. Therefore, this study aims to elucidate the relationship between BDH1 and type 2 diabetes using Mendelian randomization (MR) and to identify novel targets for the prevention and treatment of type 2 diabetes through proteomics. This study primarily employed the Mendelian Randomization (MR) method, leveraging genetic data from numerous large-scale, publicly accessible genome-wide association studies (GWAS).
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January 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.
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