In 2015, the Zambian government and the Swedish International Development Cooperation Agency (Sida) signed an agreement in which Sida committed to funding a program for Reproductive, Maternal, Newborn, Child, Adolescent Health and Nutrition (RMNCAH). The program includes a results-based financing (RBF) model that aims to reward Zambian districts for improved district-wide results on relevant indicators with additional funding. We aimed to describe stakeholders' knowledge of the RBF model and perceptions of the incentive structure during the first 18 months of the program's implementation. This study illuminates the possible pitfalls of implementing an RBF scheme without giving attention to all necessary steps of the process. A qualitative case study was used and included a review of documents, in-depth interviews, and observations. From February-April 2017, we conducted 37 in-depth interviews, representing the views of 12 development partner agencies, government departments, and health facility staff throughout Zambia. We used a qualitative framework analysis. Findings show that the Zambian government and Sida had different perceptions on what levels of the health system RBF will incentivize and that most districts and hospital administrators interviewed were unaware of the indicators that the RBF was part of the RMNCAH program at all. The lack of knowledge about the RBF scheme among respondents suggests the possibility that the model did not ultimately have the necessary preconditions to create an effective incentive structure. These results demonstrate the need for improved communication between stakeholders and the importance of sufficiently planning an RBF model before implementation.
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http://dx.doi.org/10.9745/GHSP-D-20-00463 | DOI Listing |
BMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFBiodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
View Article and Find Full Text PDFPrev Vet Med
December 2024
Department of Genetics, Animal Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Krakow, al. Mickiewicza 24/28, Krakow 30-059, Poland. Electronic address:
The purpose of the paper was to apply an Artificial Neural Networks with Radial Basis Function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes. The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows.
View Article and Find Full Text PDFHeliyon
December 2024
School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.
In this study, modeling and optimization of Hydrothermal Carbonization (HTC) of Poultry litter were conducted to convert it into high-value materials. The aim was to understand the process and predict the effect of the influencing parameters on the product properties. The recovery of Inorganic Phosphorous (IP) and Carbon (C) was regarded as the model's response, although temperature and reaction time were thought to be important variables.
View Article and Find Full Text PDFFront Cardiovasc Med
December 2024
Chief of Cardiac Surgery, Peking Union Medical College Hospital, Beijing, China.
Introduction: Acute kidney injury (AKI) is notably prevalent after cardiac surgery for patients with active infective endocarditis. This study aims to create a machine learning model to predict AKI in this high-risk group, improving upon existing models by focusing specifically on endocarditis-related surgeries.
Methods: We analyzed medical records from 527 patients who underwent cardiac surgery for active infective endocarditis from January 2012 to December 2023.
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