Deze rapportage omvat de evaluatie van de pilot Open Science Support Desk (OSSD). Het bemensen van de OSSD is een van de activiteiten die erop gericht zijn om de kwaliteit van het onderzoek dat uitgevoerd wordt in de faculteiten Gezondheid (FG), Bewegen, Sport en Voeding (FBSV) en Digitale Media en Creatieve Industrie (FDMCI) te ondersteunen in het kader van de SIA SPRONG subsidie Mensen in Beweging die in 2018 werd toegekend. Bij de OSSD kunnen Urban Vitality onderzoekers terecht voor individueel advies over kwantitatief en kwalitatief onderzoek, open science en over datamanagement. Deze evaluatie bestrijkt de pilotperiode tussen september 2019 en juni 2020. De evaluatie richt zich op:1. De mening van de gebruikers over de dienstverlening van OSSD;2. De motivatie van niet-gebruikers om geen gebruik te maken van OSSD;3. Het inventariseren van wensen van (potentiële) gebruikers voor OSSD diensten;4. Het geven van aanbevelingen voor de organisatie en toekomst van de OSSD diensten. GegevensverzamelingGegevens zijn verzameld m.b.v. twee verschillende vragenlijsten: Eén vragenlijst voor gebruikers van OSSD en één vragenlijst voor niet-gebruikers die wel tot de doelgroep horen.Daarnaast zijn gegevens gebruikt die in een excel databestand zijn bijgehouden over de dienstverlening, zoals aan wie waarover advies is gegeven en hoeveel tijd daaraan is besteed.ResultatenOSSD-gebruikers waren zeer tevreden over onze diensten en hoe deze werden geleverd. Iets minder hoog scoort de duidelijkheid van waarmee men bij de OSSD kan aankloppen. De onderzoekers die geen gebruik hadden gemaakt van de diensten van de OSSD wisten niet dat hij bestond, waarvoor ze bij de desk terecht kunnen, of hadden geen vragen. Een kanttekening is hierbij dat slechts een kwart van de niet-gebruikers de vragenlijst hebben ingevuld.Een meerderheid van de gebruikers en niet-gebruikers lijkt geïnteresseerd in deelname aan journal clubs, hulp bij literatuur zoeken en inloopspreekuren. Verder zijn onder OSSD-gebruikers de belangrijkste onderwerpen voor nieuwe dienstverlening journal clubs over statistiek, datavisualisatie, kwalitatieve analyse, kwalitatieve onderzoeksmethoden, kwantitatieve methoden en open science-tools. De belangrijkste taken voor de OSSD zijn volgens zowel gebruikers als niet-gebruikers advies, co-auteurschap en (data-) analytische ondersteuning. Conclusie De OSSD is geraadpleegd door ongeveer de helft van de potentiële gebruikers. De onderzoekers die advies hebben gekregen zijn (zeer) tevreden over de inhoud van de adviezen en over andere aspecten van de dienstverlening, zoals snelheid van reageren op vragen en de sfeer waarin de consultaties werden uitgevoerd. Daarnaast bestaat er een relatief grote groep die geen gebruik heeft gemaakt van de OSSD. De belangrijkste reden voor het niet gebruiken van de desk lijkt onbekendheid. Dit heeft mogelijk te maken met de huidige onduidelijke positie en inbedding van de OSSD. Aanbevelingen1. Formaliseer de OSSD binnen het Urban Vitality Center of Expertise (UV) of op faculteitsniveau2. Stroomlijn de rol van de OSSD in de procedures voorafgaand aan en na toekenning van subsidie en stem deze af met IXA3. Neem de 14 Open Science principes op in het UV-beleid4. Zorg er (middels beleid) voor dat de OSSD in een vroeg stadium bij nieuwe onderzoeksvoorstellen betrokken wordt5. Vervul tijdig de vacature die ontstaat voor een kwalitatief methodoloog6. Formaliseer de posities van privacy officer en informatiespecialist binnen OSSD7. Maak glashelder welke lectoraten de OSSD bedient8. Maak bij een promotieproject duidelijk welke verantwoordelijkheden liggen bij de verschillende instellingen die bij de promotie betrokken zijn9. Maak een toegankelijk content management systeem om inzicht te hebben in en te kunnen leren van lopend onderzoek10. Bespreek dit rapport en de aanbevelingen in de stuurgroepen van MiB en van UV en in het management van FG, FBSV en FDMCI.
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The term crowdsourcing was introduced by Jeff Howe (2006). It is the act of a company or organisation to take a function once performed by employees and outsourcing it to an undefined, and usually large, network of people in the form of an open call. As communication tools to organize work have become widely available, and a well-educated global work force has come online, crowdsourcing has become an increasingly important mechanism to organize work. We discuss a categorisation of crowdsourcing, its costs and benefits and several examples. The use of crowdsourcing begins with the question which strategic goal an organisation wants to achieve, and whether the benefits outweigh the costs. We give some recommendations for adopting crowdsourcing. This usually requires a certain amount of restructuring of existing workflows and a willingness to become more open which may or may not be a welcome side effect.
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Although systematic reviews are considered as central components in evidence-based practice, they currently face an important challenge to keep up with the exponential publication rate of clinical trials. After initial publication, only a minority of the systematic reviews are updated, and it often takes multiple years before these results become accessible. Consequently, many systematic reviews are not up to date, thereby increasing the time-gap between research findings and clinical practice. A potential solution is offered by a living systematic reviews approach. These types of studies are characterized by a workflow of continuous updates which decreases the time it takes to disseminate new findings. Although living systematic reviews are specifically designed to continuously synthesize new evidence in rapidly emerging topics, they have also considerable potential in slower developing domains, such as rehabilitation science. In this commentary, we outline the rationale and required steps to transition a regular systematic review into a living systematic review. We also propose a workflow that is designed for rehabilitation science.
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Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.
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Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult’s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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This article examines how collaborative design practices in higher education are reshaped through postdigital entanglement with generative artificial intelligence (GenAI). We collectively explore how co-design, an inclusive, iterative, and relational approach to educational design and transformation, expands in meaning, practice, and ontology when GenAI is approached as a collaborator. The article brings together 19 authors and three open reviewers to engage with postdigital inquiry, structured in three parts: (1) a review of literature on co-design, GenAI, and postdigital theory; (2) 11 situated contributions from educators, researchers, and designers worldwide, each offering practice-based accounts of co-design with GenAI; and (3) an explorative discussion of implications for higher education designs and futures. Across these sections, we show how GenAI unsettles assumptions of collaboration, knowing, and agency, foregrounding co-design as a site of ongoing material, ethical, and epistemic negotiation. We argue that postdigital co-design with GenAI reframes educational design as a collective practice of imagining, contesting, and shaping futures that extend beyond human knowing.
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Standard SARS-CoV-2 testing protocols using nasopharyngeal/throat (NP/T) swabs are invasive and require trained medical staff for reliable sampling. In addition, it has been shown that PCR is more sensitive as compared to antigen-based tests. Here we describe the analytical and clinical evaluation of our in-house RNA extraction-free saliva-based molecular assay for the detection of SARS-CoV-2. Analytical sensitivity of the test was equal to the sensitivity obtained in other Dutch diagnostic laboratories that process NP/T swabs. In this study, 955 individuals participated and provided NP/T swabs for routine molecular analysis (with RNA extraction) and saliva for comparison. Our RT-qPCR resulted in a sensitivity of 82,86% and a specificity of 98,94% compared to the gold standard. A false-negative ratio of 1,9% was found. The SARS-CoV-2 detection workflow described here enables easy, economical, and reliable saliva processing, useful for repeated testing of individuals.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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Purpose – In the domain of healthcare, both process efficiency and the quality of care can be improved through the use of dedicated pervasive technologies. Among these applications are so-called real-time location systems (RTLS). Such systems are designed to determine and monitor the location of assets and people in real time through the use of wireless sensor networks. Numerous commercially available RTLS are used in hospital settings. The nursing home is a relatively unexplored context for the application of RTLS and offers opportunities and challenges for future applications. The paper aims to discuss these issues. Design/methodology/approach – This paper sets out to provide an overview of general applications and technologies of RTLS. Thereafter, it describes the specific healthcare applications of RTLS, including asset tracking, patient tracking and personnel tracking. These overviews are followed by a forecast of the implementation of RTLS in nursing homes in terms of opportunities and challenges. Findings – By comparing the nursing home to the hospital, the RTLS applications for the nursing home context that are most promising are asset tracking of expensive goods owned by the nursing home in orderto facilitate workflow and maximise financial resources, and asset tracking of personal belongings that may get lost due to dementia. Originality/value – This paper is the first to provide an overview of potential application of RTLS technologies for nursing homes. The paper described a number of potential problem areas that can be addressed by RTLS. Published by Emerald Publishing Limited Original article: https://doi.org/10.1108/JET-11-2017-0046 For this paper Joost van Hoof received the Highly Recommended Award from Emerald Publishing Ltd. in October 2019: https://www.emeraldgrouppublishing.com/authors/literati/awards.htm?year=2019
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