Gathering evidence on complex workplace health promotion interventions faces methodological challenges. Therefore, the application of logic models as a theory of change is recommended to support outcome and process evaluations. The present study explores challenges and opportunities of applying logic models in application-oriented intervention research on workplace health promotion. A focus group (n = 6), consisting of scientists and workplace health promotion practitioners, was conducted using a semi-structured interview guide. The recorded qualitative data were transcribed and analysed using the structuring content analysis method. According to the focus group, logic models provide several opportunities for planning and evaluating complex workplace health promotion interventions. Logic models support the communication between science and practice, and have benefits for the provider of workplace health promotion interventions. The main challenges in working with logic models were dealing with the complex and constantly developing intervention and with the derivation and implementation of reasonable evaluation methods. The focus group exposed repeated application and a shared understanding between stakeholders as facilitators for working with logic models. In conclusion, at the science-practice interface, logic models could enhance the integrative understanding and the further development of evidence-based workplace health promotion.
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http://dx.doi.org/10.1016/j.evalprogplan.2023.102347 | DOI Listing |
Phys Chem Chem Phys
March 2025
Tianjin Key Laboratory of Film Electronic & Communicate Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Two-dimensional ferromagnetic materials have a broader development prospect in the field of spintronics. In particular, the high spin polarization system with half-metallic characteristics can be used as an efficient spin injection electrode. first-principles calculations, we predict that monolayer MnF has Dirac half-metallic properties.
View Article and Find Full Text PDFQuant Plant Biol
February 2025
Australian Research Council Centre of Excellence in Plants for Space, School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia.
Plant synthetic biology is a rapidly advancing multidisciplinary research area that applies engineering principles to design, construct, and implement new plant capabilities at the molecular, cellular, and whole organism scales. Synthetic gene circuits are important tools for enabling increased customizability in the control of gene expression in plants, with widespread applications spanning new approaches for basic biology to introduction of new traits for advancing agriculture. In this review, we first aimed to provide a comprehensive understanding of synthetic circuits.
View Article and Find Full Text PDFNat Commun
March 2025
Max Planck Institute for Polymer Research, 55128, Mainz, Germany.
Biomolecular condensates formed by proteins and nucleic acids are critical for cellular processes. Macromolecule-based coacervate droplets formed by liquid-liquid phase separation serve as synthetic analogues, but are limited by complex compositions and high molecular weights. Recently, short peptides have emerged as an alternative component of coacervates, but tend to form metastable microdroplets that evolve into rigid nanostructures.
View Article and Find Full Text PDFPLoS One
March 2025
Electrical and Information Engineering College, Hunan Institute of Engineering, Xiangtan, Hunan Province, China.
SOC prediction is of great value to electric vehicle status assessment. Informer model is better than other models in SOC prediction, but there is still a gap in practical application. Therefore, based on the health assessment algorithm, a new optimized Informer model is proposed to predict SOC.
View Article and Find Full Text PDFPLoS One
March 2025
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China.
To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability.
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