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Introduction: There is increasing work towards drawing on theory, implementing co-production and accounting for complexity within the production of systematic reviews for public health. In this paper, we report on the process of co-producing a theory; in this case, a graphical articulation of theory in the form of a logic model, which describes how contextual factors influence children's health.
Methods: We undertook a series of three online co-production workshops, involving 18-20 participants in each, and worked with an advisory group of experts with professional and lived expertise. An online virtual whiteboard was used to support the identification of factors that contributed to poorer childhood health, explanations for these factors, and connections between different factors.
Results: Driven by government strategy, we initially focussed our work on childhood obesity. However, co-production was transformational in switching the focus of the logic model away from a narrow focus on Body Mass Index as a measure of obesity, to a more holistic theory of factors that shape children's health, recognised as the intersection between healthy eating, physical activity and mental well-being. Theorising with a diverse range of co-producers helped us to recognise the stigmatising impacts that an exclusive focus on clinical measures of children's health can have, and the way that a narrow clinical focus inhibits theorising the complexity and drivers of poorer health.
Conclusion: Co-production led to a switch in theorising away from narratives of children's health that focus closely on personal responsibility, towards narratives that explore structural and contextual drivers of health.
Patient Or Public Contribution: The logic model was entirely driven by the contributions of researchers, those with lived experience (e.g., as parents and/or who have experienced poor health), and those with professional experience (e.g., as teachers) who worked together to co-produce the model. An advisory group composed of people with a similar range of expertise helped to shape the conduct of co-production and dissemination (including in the preparation of this manuscript).
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http://dx.doi.org/10.1111/hex.70346 | DOI Listing |
mSphere
September 2025
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Control of intracellular pathogens is a critical element of host defense. Defining the molecular mechanisms by which the host restricts or eliminates these pathogens may inform the development of novel immunotherapeutics and antimicrobial strategies, particularly in the face of rising antibiotic resistance. In parallel, understanding how pathogens subvert these immune responses may yield new approaches to disrupt virulence rather than viability.
View Article and Find Full Text PDFFront Psychol
August 2025
Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Psychology's crises (e.g., replicability, generalisability) are currently believed to derive from Questionable Research Practices (QRPs), thus scientific misconduct.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
International Communication College, Jilin International Studies University, Changchun, Jilin, China.
Background: Conventional automated writing evaluation systems typically provide insufficient support for students with special needs, especially in tonal language acquisition such as Chinese, primarily because of rigid feedback mechanisms and limited customisation.
Objective: This research develops context-aware Hierarchical AI Tutor for Writing Enhancement(CHATWELL), an intelligent tutoring platform that incorporates optimised large language models to deliver instantaneous, customised, and multi-dimensional writing assistance for Chinese language learners, with special consideration for those with cognitive learning barriers.
Methods: CHATWELL employs a hierarchical AI framework with a four-tier feedback mechanism designed to accommodate diverse learning needs.