Het Meta-Model of Interprofessional Development wordt beschreven. Deze bestaat uit diverse factoren die prioriteit zouden moeten krijgen om verschillende beroepsgroepen complementair te laten samenwerken om zo tot optimale en gezamenlijke uitkomsten te kunnen komen. Deze factoren vormen tezamen een systemische samenhang omdat zij een wisselwerking op elkaar hebben. Diverse wetenschappelijk toetsbare assumpties worden beschreven voor elk van deze factoren (fasen) in relatie tot interprofessionele ontwikkeling en in relatie tot hun onderlinge samenhang.
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A Meta-Model of Interprofessional Development is proposed as an integrated theory-based and procedure-centered roadmap for interprofessional collaboration. The Model is designed to inform and connect interprofessional priorities and integrate perspectives of interprofessional practice, education and research. Its purpose is to provide a comprehensive and integrated guide to enhance interprofessional collaboration given any context and/or purpose. The model proposes an operational and a strategic dimension that build on a diversity of profession-specific expertise. These dimensions consist of developmental phases related to one or more interprofessional priorities. The operational dimension and the strategic dimension influence each other. All phases influence practice, education and research perspectives. The meta-model states that each interprofessional priority is subject to practice, teachable through education, and verifiable by science. While interprofessional activity may have a common ground; priorities do result in different professional contributions and activities depending on perspective, context, and purpose. This common ground of interprofessional priorities consists of negotiating and appreciating professional identity related to role clarity, shared problem domains, different and complementary approaches to solve a shared problem, interprofessional planning and execution of an interprofessional plan. In addition, interprofessional collaboration depends on other priorities such as engagement, effort and influence at individual, collective and systemic levels. These priorities involve interprofessional identity formation, networks and/or community of practice challenges and the systemic influence of power, policy and politics. Developing interprofessional collaboration is a complex process and the authors hope this meta-model will help students, educators, practitioners and researchers unpack the complexities of interprofessional collaboration.
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From the article: Business rules management is a mean by which an organization realizes controllability of business activities to fulfill goals. Currently the focus of controllability is mainly on effectiveness, efficiency and output quality. Little attention is paid to risk, stakeholder concerns and high level goals. The purpose of this work is to present a viewpoint relating business rules management with concepts of risks, stakeholder, concerns and goals. The viewpoint is presented by means of a meta-model existing out of six concepts: stakeholder, concern, goal, business rule, requirements and implementation mechanism. In a case study the proposed view is validated in terms of completeness, usability and accuracy. Results illustrate the completeness, usability and a high degree of accuracy of our defined view. Future research is suggested on the development of a modeling language to improve the communicational value and ease of use of the meta-model.
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Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.
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Het boek ‘Create Health Ways of Working: Insights from ten eHealth innovation research projects’ presenteert inzichten uit het meta-onderzoeksproject ‘Create & Health Innovation WAys of Working Analysis’, ook wel CHIWAWA genoemd. Binnen dit meta-onderzoeksproject inventariseerden onderzoekers van de Hogeschool Utrecht (Lectoraat Onderzoekend Vermogen en Lectoraat Co-design) het gebruik van creatieve manieren van werken bij innovatieprocessen in de zorg, waarvoor zij tien onderzoeksprojecten van Nederlandse kennisinstellingen volgende in de periode 2018 – 2022. Deze tien onderzoeksprojecten en het meta-onderzoek waren onderdeel van het ZonMw-programma Create Health. Het boek presenteert case-portretten van de tien onderzoeksprojecten naar eHealth innovatie die zich concentreerden rondom de thema’s dementie, eenzaamheid en overgewicht. Vervolgens geeft het boek verdieping met betrekking tot de creatieve manieren van werken in de tien Create Health-onderzoeksprojecten, begrip van relationele processen bij het creëren van kennisuitwisseling en zicht op de impact die een dergelijke samenwerking heeft op de zorg- en welzijnssector en op de creatieve industrie. Het boek bevat aanbevelingen voor toekomstige onderzoeksconsortia, financiers en de praktijk (creatieve industrie, zorgsector en doelgroep) en sluit af met de beschrijving van een tool die gebaseerd is op het Research Pathway Model, dat als instrument gebruikt kan worden om het gesprek tussen stakeholders van innovatieprocessen in de zorg te ondersteunen.
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The metabolic syndrome (MetS) comprises cardiometabolic risk factors frequently found in individuals with obesity. Guidelines to prevent or reverse MetS suggest limiting fat intake, however, lowering carbohydrate intake has gained attention too. The aim for this review was to determine to what extent either weight loss, reduction in caloric intake, or changes in macronutrient intake contribute to improvement in markers of MetS in persons with obesity without cardiometabolic disease. A meta-analysis was performed across a spectrum of studies applying low-carbohydrate (LC) and low-fat (LF) diets. PubMed searches yielded 17 articles describing 12 separate intervention studies assessing changes in MetS markers of persons with obesity assigned to LC (<40% energy from carbohydrates) or LF (<30% energy from fat) diets. Both diets could lead to weight loss and improve markers of MetS. Meta-regression revealed that weight loss most efficaciously reduced fasting glucose levels independent of macronutrient intake at the end of the study. Actual carbohydrate intake and actual fat intake at the end of the study, but not the percent changes in intake of these macronutrients, improved diastolic blood pressure and circulating triglyceride levels, without an effect of weight loss. The homeostatic model assessment of insulin resistance improved with both diets, whereas high-density lipoprotein cholesterol only improved in the LC diet, both irrespective of aforementioned factors. Remarkably, changes in caloric intake did not play a primary role in altering MetS markers. Taken together, these data suggest that, beyond the general effects of the LC and LF diet categories to improve MetS markers, there are also specific roles for weight loss, LC and HF intake, but not reduced caloric intake, that improve markers of MetS irrespective of diet categorization. On the basis of the results from this meta-analysis, guidelines to prevent MetS may need to be re-evaluated.
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Internet technology offers a lot of new opportunities for the dissemination of information, sharing of support and consultation of professionals. Innovating professionals from multiple disciplines have begun to exploit the new opportunities for parenting support. The studies presented in this book are meant to deepen our insights in the subject of online parenting support and investigate the feasibility to use single session email consultation to empower parents. This publication includes: - A systematic review of 75 studies on online parenting support. - A meta-analytic review of 12 studies on online tools to improve parenting. - A content analysis of 129 parenting questions and responses in single session email consultation. - An analysis and validation study of the newly developed Guiding the Empowerment Process model. - An evaluation study of the effects of single session email consultation on parental empowerment. The results of this research indicate that the Internet is not only a source of information, but it can also be an instrument for support and training, aiming to improve parental competencies.
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Study selection: Randomized controlled trials published after 2007 with (former) healthcare patients ≥ 21 years of age were included if physical activity was measured objectively using a wearable monitor for both feedback and outcome assessment. The main goal of included studies was promoting physical activity. Any concurrent strategies were related only to promoting physical activity. Data extraction: Effect sizes were calculated using a fixed-effects model with standardized mean difference. Information on study characteristics and interventions strategies were extracted from study descriptions. Data synthesis: Fourteen studies met the inclusion criteria (total n = 1,902), and 2 studies were excluded from meta-analysis. The overall effect size was in favour of the intervention groups (0.34, 95% CI 0.23–0.44, p < 0.01). Study characteristics and intervention strategies varied widely. Conclusion: Healthcare interventions using feedback on objectively monitored physical activity have a moderately positive effect on levels of physical activity. Further research is needed to determine which strategies are most effective to promote physical activity in healthcare programmes. Lay Abstract Wearable technology is progressively applied in health care and rehabilitation to provide objective insight into physical activity levels. In addition, feedback on physical activity levels delivered by wearable monitors might be beneficial for optimizing their physical activity. A systematic review and meta-analysis was conducted to evaluate the effectiveness of interventions using feedback on objectively measured physical activity in patient populations. Fourteen studies including 1902 patients were analyzed. Overall, the physical activity levels of the intervention groups receiving objective feedback on physical activity improved, compared to the control groups receiving no objective feedback. Mostly, a variety of other strategies were applied in the interventions next to wearable technology. Together with wearable technology, behavioral change strategies, such as goal-setting and action planning seem to be an important ingredient to promote physical activity in health care and rehabilitation. LinkedIn: https://www.linkedin.com/in/hanneke-braakhuis-b9277947/ https://www.linkedin.com/in/moniqueberger/
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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