Motivating the agricultural industry to engage in digital transformation is a challenge academically and socially. It is of great significance to study the choice of digital transformation mode of agricultural industrial organization and analyze its driving factors for promoting the sustainable development of agricultural industrial organization. This study adopts a bilateral evolutionary game to construct a decision-making model for behavioral decision-making during the digital transformation of the agricultural industry. The contingent-actual logical framework and multiple case studies of Yunnan highland agriculture are used to explore the impact of various factors on behavioral decision-making during the digital transformation of the agricultural industry. Additionally, a simulation analysis is used to verify the validity of the bilateral evolutionary game model. The results demonstrate that: (1) When the agricultural industry chooses "active transformation," behavioral decision-making during the digital transformation of the agricultural industry reaches a Nash equilibrium; (2) transformation costs, industry revenue, and reward and penalty mechanisms are the main driving factors for whether or not the agricultural industry chooses to actively engage in digital transformation; and (3) the probability of active digital transformation increases when agricultural industry organizations obtain higher returns at lower costs. Simultaneously, the higher the government's incentives, the greater the enthusiasm. However, when the penalty is excessive, the digital transformation takes the shape of either passive transformation or forced active transformation. Subsequently, it is necessary to improve the digital transformation planning of the agricultural industry, strengthen this field's cooperation mechanism, and formulate a reasonable reward and penalty system for digital transformation.
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http://dx.doi.org/10.1016/j.jenvman.2024.120881 | DOI Listing |
JMIR Infodemiology
January 2025
Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises.
View Article and Find Full Text PDFSex Health
January 2025
Department of General Practice and Primary Care, Melbourne Medical School, University of Melbourne, Carlton, Vic, Australia; and Family Medicine and Primary Care, LKC Medicine, Nanyang Technological University, Singapore, Singapore.
Background Gonorrhoea notification rates in Australia have more than doubled between 2014 and 2019. We explored gonorrhoea testing patterns and management of gonorrhoea infection in general practice. Methods We analysed de-identified electronic medical record data for individuals who attended 73 Australian general practices (72 in the state of Victoria) between January 2018 and December 2020.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Department of Medical Oncology, Assistance Publique - Hôpitaux de Paris, Henri Mondor Teaching Hospital, Créteil, France, 1 Rue Gustave Eiffel, 94000.
Purpose: Using electronic patient-reported outcomes (ePRO) in clinical trial has shown benefits for patients. However, the digital divide can lead to unequal access to telehealth. We investigated whether a dedicated support program could bridge that divide.
View Article and Find Full Text PDFEur J Transl Myol
January 2025
A&C M-C Foundation for Translational Myology, Padua, Italy; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Digital Transformation, Landspitali University Hospital, Reykjavík.
We invariably hear that Artificial Intelligence (AI), a rapidly evolving technology, does not just creatively assemble known knowledge. We are told that AI learns, processes and creates, starting from fixed points to arrive at innovative solutions. In the case of scientific work, AI can generate data without ever having entered a laboratory, (i.
View Article and Find Full Text PDFBull World Health Organ
February 2025
LSE Health, Department of Health Policy, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, London, England.
Objective: To map how social, commercial, political and digital determinants of health have changed or emerged during the recent digital transformation of society and to identify priority areas for policy action.
Methods: We systematically searched MEDLINE, Embase and Web of Science on 24 September 2023, to identify eligible reviews published in 2018 and later. To ensure we included the most recent literature, we supplemented our review with non-systematic searches in PubMed® and Google Scholar, along with records identified by subject matter experts.
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