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Identifying the facilitators of Iran's alcoholic beverage black market and presenting solutions for harm reduction: a qualitative document analysis.
Harm Reduct J
December 2024
Department of Health Policy and Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Background: Despite Iran's prohibition politics regarding alcoholic beverages consumption, marketing, and trading, there is a flourishing black market. Often, alcohol producers on this black market do not adhere alcohol production standards, resulting in a lot of deaths and significant consequences each year. Accordingly, this study was carried out to identify facilitators for the growth of the black market for alcoholic beverages in Iran and provide solutions for harm reduction.
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BMC Infect Dis
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Lab Services and Infection Control; Chief, Education and Research, Artemis Hospitals, Sector-51, Gurugram, Haryana, India.
Klebsiella pneumoniae, a pathogen of concern worldwide can be classified as classical K. pneumoniae (cKp) and Hypervirulent K. pneumoniae (HvKp).
View Article and Find Full Text PDFClassification of influencing factors of speaking-up behaviour in hospitals: a systematic review.
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Centre for Safety in Healthcare, at the Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, Delft, 2628 BX, the Netherlands.
Background: Speaking up among healthcare professionals plays an essential role in improving patient safety and quality of care, yet it remains complex and multifaceted behaviour. Despite awareness of potential risks and adverse outcomes for patients, professionals often hesitate to voice concerns due to various influencing factors. This complexity has encouraged research into the determinants of speaking-up behaviour in hospital settings.
View Article and Find Full Text PDFBias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.
BMC Public Health
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Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFPrevalence and risk factors associated with birth asphyxia among neonates delivered in China: a systematic review and meta-analysis.
BMC Pediatr
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Department of Nursing, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: Birth asphyxia is a critical condition caused by an insufficient oxygen supply during delivery, and it poses a major threat to the health of newborns. The present meta-analysis aimed to estimate the prevalence of birth asphyxia among neonates and identify its risk factors in China.
Methods: PubMed, EMBASE, Scopus, Web of Science, the China Academic Journals (CNKI), the Chinese Biomedical Literature (CBM), the China Science and Technology Journal Database (VIP), and the WanFang database were searched for related publications.
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