Aims and objectives: To describe the process of implementing evidence-based practice (EBP) in a clinical nursing setting. Background: EBP has become a major issue in nursing, it is insufficiently integrated in daily practice and its implementation is complex. Design: Participatory action research. Method: The main participants were nurses working in a lung unit of a rural hospital. A multi-method process of data collection was used during the observing, reflecting, planning and acting phases. Data were continuously gathered during a 24-month period from 2010 to 2012, and analysed using an interpretive constant comparative approach. Patients were consulted to incorporate their perspective. Results: A best-practice mode of working was prevalent on the ward. The main barriers to the implementation of EBP were that nurses had little knowledge of EBP and a rather negative attitude towards it, and that their English reading proficiency was poor. The main facilitators were that nurses wanted to deliver high-quality care and were enthusiastic and open to innovation. Implementation strategies included a tailored interactive outreach training and the development and implementation of an evidence-based discharge protocol. The academic model of EBP was adapted. Nurses worked according to the EBP discharge protocol but barely recorded their activities. Nurses favourably evaluated the participatory action research process. Conclusions: Action research provides an opportunity to empower nurses and to tailor EBP to the practice context. Applying and implementing EBP is difficult for front-line nurses with limited EBP competencies. Relevance to clinical practice: Adaptation of the academic model of EBP to a more pragmatic approach seems necessary to introduce EBP into clinical practice. The use of scientific evidence can be facilitated by using pre-appraised evidence. For clinical practice, it seems relevant to integrate scientific evidence with clinical expertise and patient values in nurses’ clinical decision making at the individual patient level.
This study explores the evaluation of research pathways of self-management health innovations from discovery to implementation in the context of practice-based research. The aim is to understand how a new process model for evaluating practice-based research provides insights into the implementation success of innovations. Data were collected from nine research projects in the Netherlands. Through document analysis and semi-structured interviews, we analysed how the projects start, evolve, and contribute to the healthcare practice. Building on previous researchevaluation approaches to monitor knowledge utilization, we developed a Research Pathway Model. The model’s process character enables us to include and evaluate the incremental work required throughout the lifespan of an innovation project and it helps to foreground that innovation continues during implementation in real-life settings. We found that in each researchproject, pathways are followed that include activities to explore a new solution, deliver a prototype and contribute to theory. Only three projects explored the solution in real life and included activities to create the necessary changes for the solutions to be adopted. These three projects were associated with successful implementation. The exploration of the solution in a real-life environment in which users test a prototype in their own context seems to be a necessaryresearch activity for the successful implementation of self-management health innovations.
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Background: Although principles of the health promoting school (HPS) approach are followed worldwide, differences between countries in the implementation are reported. The aim of the current study was (1) to examine the implementation of the HPS approach in European countries in terms of different implementation indicators, that is, percentage of schools implementing the HPS approach, implementation of core components, and positioning on so‐called HPS‐related spectra, (2) to explore patterns of consistency between the implementation indicators across countries, and (3) to examine perceived barriers and facilitators to the implementation of the HPS approach across countries. Methods: This study analyzed data from a survey that was part of the Schools for Health in Europe network's Monitoring Task 2020. The survey was completed by HPS representatives of 24 network member countries. Results: Large variations exist in (the influencing factors for) the implementation of the HPS approach in European countries. Observed patterns show that countries with higher percentages of schools implementing the HPS approach also score higher on the implementation of the core components and, in terms of spectra, more toward implementing multiple HPS core components, add‐in strategies, action‐oriented research and national‐level driven dissemination. In each country a unique mix of barriers and facilitators was observed. Conclusion: Countries committed to implementing the HPS approach in as many schools as possible also seem to pay attention to the quality of implementation. For a complete and accurate measurement of implementation, the use of multiple implementation indicators is desirable.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
