Abstract Aims: To lower the threshold for applying ultrasound (US) guidance during peripheral intravenous cannulation, nurses need to be trained and gain experience in using this technique. The primary outcome was to quantify the number of procedures novices require to perform before competency in US-guided peripheral intravenous cannulation was achieved. Materials and methods: A multicenter prospective observational study, divided into two phases after a theoretical training session: a handson training session and a supervised life-case training session. The number of US-guided peripheral intravenous cannulations a participant needed to perform in the life-case setting to become competent was the outcome of interest. Cusum analysis was used to determine the learning curve of each individual participant. Results: Forty-nine practitioners participated and performed 1855 procedures. First attempt cannulation success was 73% during the first procedure, but increased to 98% on the fortieth attempt (p<0.001). The overall first attempt success rate during this study was 93%. The cusum learning curve for each practitioner showed that a mean number of 34 procedures was required to achieve competency. Time needed to perform a procedure successfully decreased when more experience was achieved by the practitioner, from 14±3 minutes on first procedure to 3±1 minutes during the fortieth procedure (p<0.001). Conclusions: Competency in US-guided peripheral intravenous cannulation can be gained after following a fixed educational curriculum, resulting in an increased first attempt cannulation success as the number of performed procedures increased.
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Sustainability transition research seeks to understand the patterns and dynamics of structural societal change as well as unearth strategies for governance. However, existing frameworks emphasize innovation and build-up over exnovation and break-down. This limits their potential in making sense of the turbulent and chaotic dynamics of current transition-in-the-making. Addressing this gap, our paper elaborates on the development and use of the X-curve framework. The X-curve provides a simplified depiction of transitions that explicitly captures the patterns of build-up, breakdown, and their interactions.Using three cases, we illustrate the X-curve’s main strength as a framework that can support groups of people to develop a shared understanding of the dynamics in transitions-in-the-making. This helps them reflect upon their roles, potential influence, and the needed capacities for desired transitions. We discuss some challenges in using the X-curve framework, such as participants’ grasp of ‘chaos’, and provide suggestions on how to address these challenges and strengthen the frameworks’ ability to support understanding and navigation of transition dynamics. We conclude by summarizing its main strength and invite the reader to use it, reflect on it, build on it, and judge its value for action research on sustainability transitions themselves.
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The workforce in the EU is ageing, and this requires investment in older workers so that the organisations in which they work remain competitive and viable. One such investment takes the form of organising and facilitating intergenerational learning: learning between and among generations that can lead to lifelong learning, innovation and organisational development. However, successfully implementing intergenerational learning is complex and depends on various factors at different levels within the organisation. This multidisciplinary literature review encompasses work from the fields of cognitive psychology, occupational health, educational science, human resource development and organisational science and results in a framework that organisations can use to understand how they can create the conditions needed to ensure that the potential of their ageing workforce is tapped effectively and efficiently. Although not a comprehensive review, this chapter serves as a basis for further empirical research and gives practitioners an insight into solving a growing problem.
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In their postgraduate educational programs, residents are immersed in a complex workplace. To improve the quality of the training program, it is necessary to gain insight into the factors that influence the process of learning in the workplace. An exploratory study was carried out among 56 nursing home physicians in training (NHPT) and 62 supervisors. They participated in semi-structured group interviews, in which they discussed four questions regarding workplace learning. Qualitative analysis of the data was performed to establish a framework of factors that influence workplace learning, within which framework comparisons between groups could be made. A framework consisting of 56 factors was identified. These were grouped into 10 categories, which in turn were grouped into four domains: the working environment, educational factors in the workplace, NHPT characteristics and supervisor characteristics. Of the factors that influence workplace learning, social integration was cited most often. Supervisors more often reported educational factors and NHPTs more frequently reported impediments. Conclusion: The educational relationship may be improved when supervisors explicitly discuss the learning process and learning conditions within the workplace, thereby focusing on the NHPT needs. Special attention should be paid to the aspects of social integration. A good start could be to answer the question regarding how to establish a basic feeling of 'knowing where you are' and 'how to go about things' to make residents feel comfortable enough to focus on the learning process.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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Learning Analytics en bias – Learning analytics richt zich op het meten en analyseren van studentgegevens om onderwijs te verbeteren. Bakker onderscheidt hierin verschillende niveaus, zoals student analytics en institutional analytics, en focust op inclusion analytics, waarin gekeken wordt naar kansengelijkheid. Bias – systematische vooroordelen in data – kan vooroordelen in algoritmen versterken en zo kansenongelijkheid veroorzaken. De onderzoeksmethode maakt gebruik van het 4/5-criterium, waarbij fairness in uitkomsten gemeten wordt door te kijken of de kansen voor de beschermde groep minstens 80% zijn van die van de bevoorrechte groep.Onderzoeksaanpak – Bakker gebruikt machine learning om retentie na het eerste studiejaar te voorspellen en onderzoekt vervolgens verschillen tussen groepen studenten, zoals mbo-en vwo-studenten. Hij volgt drie stappen: (1) Data voorbereiden en modellen bouwen: Data worden opgesplitst en opgeschoond om accurate voorspelmodellen te maken. (2) Variabelen analyseren: Invloed van kenmerken op uitkomsten wordt beoordeeld voor verschillende groepen. (3) Fairness berekenen: Het 4/5-criterium wordt toegepast op metrics zoals accuraatheid en statistische gelijkheid om bias en ongelijkheden te identificeren. Resultaten, aanbevelingen en vervolgonderzoek – Uit het onderzoek blijkt dat kansengelijkheid bij veel opleidingen ontbreekt, met name voor mannen en mbo-studenten, die een hogere kans op uitval hebben. Bakker adviseert sensitieve kenmerken zoals migratieachtergrond mee te nemen in analyses op basis van informed consent. Daarnaast pleit hij voor meer flexibiliteit in het beleid, geïnspireerd door maatregelen tijdens de coronacrisis, die een positief effect hadden op studiesucces.Toekomstvisie – Bakker benadrukt dat niet elke ongelijkheid het gevolg is van discriminatie en roept op tot data-informed interventies om sociale rechtvaardigheid in het onderwijs te bevorderen. Zijn methode wordt open access beschikbaar gesteld, zodat ook andere instellingen deze kunnen toepassen en kansengelijkheid systematisch en bewust kunnen onderzoeken.
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Blended learning, a teaching format in which face-to-face and online learning is integrated, nowadays is an important development in education. Little is known, however, about its affordances for teacher education, and for domain specific didactical courses in particular. To investigate this topic, we carried out a design research project in which teacher educators engaged in a co-design process of developing and field-testing open online learning units for mathematics and science didactics. The preliminary results concern descriptions of the work processes by the design teams, of design heuristics, and of typical ways of collaborating. These findings are illustrated for the case of two of the designed online units on statistics didactics and mathematical thinking, respectively.
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The diversity and intensity of human activities in the North Sea region (NSR) and other maritime regions are increasing. This necessitates transboundary coordination at the sea basin level, which is required but yet insufficiently established. Through European co-funded projects, national policymakers, stakeholders, and scientists in MSP are enabled to develop transboundary coordination (TBC) mechanisms. TBC requires, alongside other factors, a form of social and policy learning between these actors in different countries. The NorthSEE project (2016–2022) was an example of such an EU-co-funded project and was aimed at enhancing coherence in MSP processes and plans across the NSR. This article examines the project's key learning outcomes, the role of the MSP Challenge Simulation Platform in supporting these outcomes, and factors that enabled or constrained learning within the project. Data was collected during the project via document analysis, questionnaires from participatory stakeholder workshops, interviews with sixteen project participants and ten workshop participants, and observations. The study highlights that project participants have gained more insight into each other's planning systems, are able to contact each other more easily, and have initiated several follow-up initiatives. Furthermore, it shows that interactive and participatory tools, such as the MSP Challenge Simulation Platform, can contribute to individual and social learning by providing participants with instant feedback on their decisions. These learning outcomes have been influenced by various enabling and constraining conditions, including time, resources, and the differing levels of expertise and knowledge among project partners and participants. Assessing the broader societal impact remains a challenge and warrants further attention.
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Nationwide and across the globe, the quality, affordability, and accessibility of home-based healthcare are under pressure. This issue stems from two main factors: the rapidly growing ageing population and the concurrent scarcity of healthcare professionals. Older people aspire to live independently in their homes for as long as possible. Additionally, governments worldwide have embraced policies promoting “ageing in place,” reallocating resources from institutions to homes and prioritising home-based services to honour the desire of older people to continue living at home while simultaneously addressing the rising costs associated with traditional institutional care.Considering the vital role of district nursing care and the fact that the population of older people in need of assistance at home is growing, it becomes clear that district nursing care plays a crucial role in primary care. The aim of this thesis is twofold: 1) to strengthen the evidence base for district nursing care; and 2) to explore the use of outcomes for learning and improving in district nursing care. The first part of this thesis examines the current delivery of district nursing care and explores its challenges during the COVID-19 pandemic to strengthen the evidence base and get a better understanding of district nursing care. Alongside the goal of strengthening the evidence for district nursing care, the second part of this thesis explores the use of patient outcomes for learning and improving district nursing care. It focuses on nurse-sensitive patient outcomes relevant to district nursing care, their current measurement in practice, and what is needed to use outcomes for learning and improving district nursing practice.
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