Determine background levels are a key element in the further characterisation of groundwater bodies, according to Water Framework Directive 2000/60/EC and, more specifically, Groundwater Directive 2006/118/EC. In many cases, these levels present very high values for some parameters and types of groundwater, which is significant for their correct estimation as a prior step to establishing thresholds, assessing the status of water bodies and subsequently identifying contaminant patterns. The Guadalhorce River basin presents widely varying hydrogeological and hydrochemical conditions. Therefore, its background levels are the result of the many factors represented in the natural chemical composition of water bodies in this basin. The question of determining background levels under objective criteria is generally addressed as a statistical problem, arising from the many aspects involved in its calculation. In the present study, we outline the advantages of applying two statistical techniques applied specifically for this purpose: (1) the iterative 2σ technique and (2) the distribution function, and examine whether the conclusions reached by these techniques are similar or whether they differ considerably. In addition, we identify the specific characteristics of each approach and the circumstances under which they should be used.
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http://dx.doi.org/10.1016/j.jenvman.2012.11.042 | DOI Listing |
JMIR Form Res
January 2025
Private Practice, Ballito, South Africa.
Background: Barriers to mental health assessment and intervention have been well documented within South Africa, in both urban and rural settings. Internationally, evidence has emerged for the effectiveness of technology and, specifically, app-based mental health tools and interventions to help overcome some of these barriers. However, research on digital interventions specific to the South African context and mental health is limited.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department High-Tech Business and Entrepreneurship Section, Industrial Engineering and Business Information Systems, University of Twente, Enschede, Overijssel, Netherlands.
Health recommender systems (HRS) have the capability to improve human-centered care and prevention by personalizing content, such as health interventions or health information. HRS, an emerging and developing field, can play a unique role in the digital health field as they can offer relevant recommendations, not only based on what users themselves prefer and may be receptive to, but also using data about wider spheres of influence over human behavior, including peers, families, communities, and societies. We identify and discuss how HRS could play a unique role in decreasing health inequities.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Background: Opioid medications are important for pain management, but many patients progress to unsafe medication use. With few personalized and accessible behavioral treatment options to reduce potential opioid-related harm, new and innovative patient-centered approaches are urgently needed to fill this gap.
Objective: This study involved the first phase of co-designing a digital brief intervention to reduce the risk of opioid-related harm by investigating the lived experience of chronic noncancer pain (CNCP) in treatment-seeking patients, with a particular focus on opioid therapy experiences.
Background: Assisted partner services (APSs; sometimes called index testing) are now being brought to scale as a high-yield HIV testing strategy in many nations. However, the success of APSs is often hampered by low levels of partner elicitation. The Computer-Assisted Self-Interview (CASI)-Plus study sought to develop and test a mobile health (mHealth) tool to increase the elicitation of sexual and needle-sharing partners among persons with newly diagnosed HIV.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
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