Multi-sectoral, interdisciplinary health research is increasingly recognizing integrated knowledge translation (iKT) as essential. It is characterized by diverse research partnerships, and iterative knowledge engagement, translation processes and democratized knowledge production. This paper reviews the methodological complexity and decision-making of a large iKT project called Seniors - Adding Life to Years (SALTY), designed to generate evidence to improve late life in long-term care (LTC) settings across Canada. We discuss our approach to iKT by reviewing iterative processes of team development and knowledge engagement within the LTC sector. We conclude with a brief discussion of the important opportunities, challenges, and implications these processes have for LTC research, and the sector more broadly.
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http://dx.doi.org/10.15171/ijhpm.2019.123 | DOI Listing |
ACS Nano
January 2025
Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou 215123, P. R. China.
Knowledge of localized strain at the micrometer scale is essential for tailoring the electrical and mechanical properties of ongoing thinning of crystal silicon (c-Si) solar cells. Thinning c-Si wafers below 110 m are susceptible to cracking in manufacturing due to the nonuniform stress distribution at a micrometer region, necessitating a rigorous technique to reveal the localized stress distribution correlating with its device electrical output. In this context, a Raman microscopy integrated with a photovoltage mapping setup with high resolution to the submicrometer scale is developed to acquire correlative Raman-voltage of the localized physical properties at the microcracks on the rear side of c-Si solar cells.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
View Article and Find Full Text PDFEur J Trauma Emerg Surg
January 2025
Division of Traumatology, Surgical Critical Care and Emergency Surgery, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, USA.
Purpose: Our study explores the utilization of objective tools for preoperative assessment of elderly patients by Emergency General Surgeons (EGS).
Methods: A descriptive cross-sectional survey was conducted via the European Society for Trauma and Emergency Surgery (ESTES) Research Committee. EGS were invited through the ESTES members' mailing list and social media platforms.
Introduction: Assessment of fitness for flight constitutes one of the core tasks of aeromedical professionals. The value of such evaluations depends on the decision to be based on complete medical information, valid risk methodology, and genuine flight safety indicators. To achieve these goals, the aeromedical practitioner should ensure an evidence-based approach.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections.
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