Background: Nurses play an important role in interprofessional pharmaceutical care. Curricula related to pharmaceutical care, however, vary a lot. Mapping the presence of pharmaceutical care related domains and competences in nurse educational programs can lead to a better understanding of the extent to which curricula fit expectations of the labour market. The aim of this study was to describe 1) the presence of pharmaceutical care oriented content in nursing curricula at different educational levels and 2) nursing students' perceived readiness to provide nurse pharmaceutical care in practice. Methods: A quantitative cross-sectional survey design was used. Nursing schools in 14 European countries offering educational programs for levels 4-7 students were approached between January and April 2021. Through an online survey final year students had to indicate to what extent pharmaceutical care topics were present in their curriculum. Results: A total of 1807 students participated, of whom 8% had level 4-5, 80% level 6, 12% level 7. Up to 84% of the students indicated that pharmaceutical care content was insufficiently addressed in their curriculum. On average 14% [range 0-30] felt sufficiently prepared to achieve the required pharmaceutical care competences in practice. In level 5 curricula more pharmaceutical care domains were absent compared with other levels. Conclusions: Although several pharmaceutical care related courses are present in current curricula of level 4-7 nurses, its embedding should be extended. Too many students perceive an insufficient preparation to achieve pharmaceutical care competences required in practice. Existing gaps in pharmaceutical care should be addressed to offer more thoroughly prepared nurses to the labour market.
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Background: Nurses play an important role in pharmaceutical care. They are involved in: detecting clinical change; communicating/discussing pharmacotherapy with patients, their advocates, and other healthcare professionals; proposing and implementing medication-related interventions; and ensuring follow-up of patients and medication regimens. To date, a framework of nurses' competences on knowledge, skills, and attitudes as to interprofessional pharmaceutical care tasks is missing. Objectives: To reach agreement with experts about nurses' competences for tasks in interprofessional pharmaceutical care. Methods: A two-phase study starting with a scoping review followed by five Delphi rounds was performed. Competences extracted from the literature were assessed by an expert panel on relevance by using the RAND/UCLA method. The experts (n = 22) involved were healthcare professionals, nurse researchers, and educators from 14 European countries with a specific interest in nurses' roles in interprofessional pharmaceutical care. Descriptive statistics supported the data analysis. Results: The expert panel reached consensus on the relevance of 60 competences for 22 nursing tasks. Forty-one competences were related to 15 generic nursing tasks and 33 competences were related to seven specific nursing tasks. Conclusions: This study resulted in a competence framework for competency-based nurse education. Future research should focus on imbedding these competences in nurse education. A structured instrument should be developed to assess students' readiness to achieve competence in interprofessional pharmaceutical care in clinical practice.
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Objectives: To understand healthcare professionals' experiences and perceptions of nurses' potential or ideal roles in pharmaceutical care (PC). Design: Qualitative study conducted through semi-structured in-depth interviews. Setting: Between December 2018 and October 2019, interviews were conducted with healthcare professionals of 14 European countries in four healthcare settings: hospitals, community care, mental health and long-term residential care. Participants: In each country, pharmacists, physicians and nurses in each of the four settings were interviewed. Participants were selected on the basis that they were key informants with broad knowledge and experience of PC. Data collection and analysis: All interviews were conducted face to face. Each country conducted an initial thematic analysis. Consensus was reached through a face-to-face discussion of all 14 national leads. Results: 340 interviews were completed. Several tasks were described within four potential nursing responsibilities, that came up as the analysis themes, being: 1) monitoring therapeutic/adverse effects of medicines, 2) monitoring medicines adherence, 3) decision making on medicines, including prescribing 4) providing patient education/information. Nurses' autonomy varied across Europe, from none to limited to a few tasks and emergencies to a broad range of tasks and responsibilities. Intended level of autonomy depended on medicine types and level of education. Some changes are needed before nursing roles can be optimised and implemented in practice. Lack of time, shortage of nurses, absence of legal frameworks and limited education and knowledge are main threats to European nurses actualising their ideal role in PC. Conclusions: European nurses have an active role in PC. Respondents reported positive impacts on care quality and patient outcomes when nurses assumed PC responsibilities. Healthcare professionals expect nurses to report observations and assessments. This key patient information should be shared and addressed by the interprofessional team. The study evidences the need of a unique and consensus-based PC framework across Europe.
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Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
