Structured decision making (SDM) is defined by having a variety of characteristics, including a focus on clarifying the context, defining all relevant objectives, developing alternatives, predicting consequences, evaluating tradeoffs, and monitoring and learning from decisions. Some of the most compelling aspects for public decision making are the inclusion of diverse values in the selection of objectives and the opportunity to create a shared understanding of the system, both the context and potential tradeoffs of different strategies. While the technological requirements of the most rigid SDM processes are out of reach of most public agencies and interested publics, governance structures may enable the use of different stages of SDM to improve decisions, without relying on complete datasets and extensive statistical knowledge. Building upon a 4-year participatory research project, we analyze the use of SDM with four different watershed groups to understand the governance factors that facilitated the use of SDM as a decision support tool. All groups aimed to add human wellbeing objectives to existing natural resource health objectives when making decisions. We found that who defines the objectives and required outputs of planning as well as how decisions were made influenced the extent to which groups completed SDM steps. We also demonstrate that decisions can be improved by engaging in each step of the SDM process, and the perfect decision may not depend on completing all steps.
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http://dx.doi.org/10.1007/s00267-025-02142-0 | DOI Listing |
Europace
March 2025
Clinical Cardiac Academic Group, Genetic and Cardiovascular Sciences Institute, City-St George's University of London, London, UK.
Atrial fibrillation (AF) is one of the most common cardiac diseases and a complicating comorbidity for multiple associated diseases. Many clinical decisions regarding AF are currently based on the binary recognition of AF being present or absent with the categorical appraisal of AF as continued or intermittent. Assessment of AF in clinical trials is largely limited to the time to (first) detection of an AF episode.
View Article and Find Full Text PDFSci Adv
March 2025
Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty.
View Article and Find Full Text PDFPLoS One
March 2025
Department of Population Health Sciences, School of Public Health, Georgia State University Atlanta, Atlanta, Georgia, United States of America.
Objective: Concurrent use of alcohol and cigarettes is well-documented in the literature. However, it is unclear how e-cigarette regulations in a growing number of localities impact the use of tobacco and alcohol in the US. This study aims to evaluate the impacts of excise taxes, tobacco use restrictions in restaurants/bars, and availability of alcohol flavor in e-cigarettes on tobacco consumption, and their cross impacts on alcohol consumption.
View Article and Find Full Text PDFPLoS One
March 2025
School of Foreign Studies, University of Science and Technology Beijing, Beijing, China.
Despite the growing interests in investigating the application of data-driven learning (DDL), much existing research remains outcome-oriented. Limited attention has been paid to learners' interactions with corpora, especially the experiences of consulting corpora and decision-making processes during revision in second language (L2) writing. In this regard, this study investigates how corpora assist language learning during the revision process in a classroom-based foreign language learning context.
View Article and Find Full Text PDFJ Med Internet Res
March 2025
Inverness College, University of the Highlands and Islands, Inverness, GB.
Background: Artificial intelligence (AI) is rapidly transforming healthcare, offering significant advancements in patient care, clinical workflows, and nursing education. While AI has the potential to enhance health outcomes and operational efficiency, its integration into nursing practice and education raises critical ethical, social, and educational challenges that must be addressed to ensure responsible and equitable adoption.
Objective: This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing.
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