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Holistic and multi-disciplinary responses should be prioritized given the depth and breadth through which corruption in the healthcare sector can cover. Here, taking the Peruvian context as an example, we will reflect on the issue of corruption in health systems, including corruption with roots within and outside the health sector, and ongoing efforts to combat it. Our reflection of why corruption in health systems in settings with individual and systemic corruption should be an issue that is taken more seriously in Peru and beyond aligns with broader global health goals of improving health worldwide. Addressing corruption also serves as a pragmatic approach to health system strengthening and weakens a barrier to achieving universal health coverage and Sustainable Development Goals related to health and justice. Moreover, we will argue that by pushing towards a practice of normalizing the conversation about corruption in health has additional benefits, including expanding the problematization to a wider audience and therefore engaging with communities. For young researchers and global health professionals with interests in improving health systems in the early career stages, corruption in health systems is an issue that could move to the forefront of the list of global health challenges. This is a challenge that is uniquely multi-disciplinary, spanning the health, economy, and legal sectors, with wider societal implications.
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http://dx.doi.org/10.15171/ijhpm.2019.104 | DOI Listing |
Afr J Prim Health Care Fam Med
September 2025
Department of Optometry, Faculty of Health Sciences, University of the Free State, Bloemfontein.
Background: Social media has become a platform where unheard voices within different communities are shared with government.
Aim: The study explored and described expressed reactions of social media users regarding the implementation of the National Health Insurance (NHI) in South Africa.
Setting: This study was conducted online on existing social media platforms that share current news.
Arch Iran Med
July 2025
Department of Orthopedics, Taleghani Hospital Clinical Research Development Unit, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: The healthcare system is a crucial indicator of government performance, especially in developing countries like the Islamic Republic of Iran, which has seen significant reforms in recent decades. This study explores healthcare workers' perceptions of recent government health policies.
Methods: This study was conducted as a national survey.
Magn Reson Med
August 2025
Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada.
Purpose: The purpose of this work was to develop and evaluate a novel method that leverages neural networks and physical modeling for 3D motion correction at different levels of corruption.
Methods: The novel method ("UNet+JE") combines an existing neural network ("UNet") with a physics-informed algorithm for jointly estimating motion parameters and the motion-compensated image ("JE"). UNet and UNet+JE were trained on two training datasets separately with different distributions of motion corruption severity and compared to JE as a benchmark.
J R Soc Interface
August 2025
Department of Mathematics and Statistics, Brock University, St Catharines, Ontario, Canada.
Early warning signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviours, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by different noise sources, posing challenges for time-series classification (TSC) tasks.
View Article and Find Full Text PDFPLOS Digit Health
August 2025
Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America.
Kidney cancer is among the top 10 most common malignancies in adults, and is commonly evaluated with four-phase computed tomography (CT) imaging. However, the presence of missing or corrupted images remains a significant problem in medical imaging that impairs the detection, diagnosis, and treatment planning of kidney cancer. Deep learning approaches through conditional generative adversarial networks (cGANs) have recently shown technical promise in the task of imputing missing imaging data from these four-phase studies.
View Article and Find Full Text PDF