Deze checklist is een productie van de leeropdracht Integrale aanpak kindermishandeling van de Hanzehogeschool Groningen, in samenwerking met het regionale leernetwerk Veilig Opgroeien VONK. Groningen, januari 2022.
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Purpose In this systematic literature review, the effects of the application of a checklist during in hospital resuscitation of trauma patients on adherence to the ATLS guidelines, trauma team performance, and patient-related outcomes were integrated. Methods A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Metaanalyses checklist. The search was performed in Pubmed, Embase, CINAHL, and Cochrane inception till January 2019. Randomized controlled- or controlled before-and-after study design were included. All other forms of observational study designs, reviews, case series or case reports, animal studies, and simulation studies were excluded. The Effective Public Health Practice Project Quality Assessment Tool was applied to assess the methodological quality of the included studies. Results Three of the 625 identified articles were included, which all used a before-and-after study design. Two studies showed that Advanced Trauma Life Support (ATLS)-related tasks are significantly more frequently performed when a checklist was applied during resuscitation. [14 of 30 tasks (p < 0.05), respectively, 18 of 19 tasks (p < 0.05)]. One study showed that time to task completion (− 9 s, 95% CI = − 13.8 to − 4.8 s) and workflow improved, which was analyzed as model fitness (0.90 vs 0.96; p < 0.001); conformance frequency (26.1% vs 77.6%; p < 0.001); and frequency of unique workflow traces (31.7% vs 19.1%; p = 0.005). One study showed that the incidence of pneumonia was higher in the group where a checklist was applied [adjusted odds ratio (aOR) 1.69, 95% Confidence Interval (CI 1.03–2.80)]. No difference was found for nine other assessed complications or missed injuries. Reduced mortality rates were found in the most severely injured patient group (Injury Severity score > 25, aOR 0.51, 95% CI 0.30–0.89). Conclusions The application of a checklist may improve ATLS adherence and workflow during trauma resuscitation. Current literature is insufficient to truly define the effect of the application of a checklist during trauma resuscitation on patientrelated outcomes, although one study showed promising results as an improved chance of survival for the most severely injured patients was found.
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Achtergrond Het is bekend dat gestructureerde instrumenten voor taxatie van het kortetermijnrisico bijdragen aan het voorspellen van fysiek agressief gedrag bij patiënten in de acute psychiatrie. Doel Onderzoeken of de Brøset Violence Checklist (BVC), een instrument voor de inschatting van fysieke agressie op korte termijn, kan bijdragen aan het voorspellen van fysieke agressie-incidenten binnen de forensische psychiatrie en onderzoeken hoe het gebruik van de BVC wordt ervaren. Methode Tweemaal per 24 uur op min of meer vaste momenten werd voor alle patiënten die in 2019 verbleven op een crisisafdeling binnen een forensisch psychiatrisch centrum een BVC-score geregistreerd. De totaalscores van de BVC werden vervolgens gerelateerd aan fysieke agressie-incidenten. Daarnaast werden focusgroepen en interviews gehouden met sociotherapeuten om de ervaringen met het gebruik van de BVC te onderzoeken. Resultaten Uit de analyse kwam een significante voorspellende waarde van de BVC-totaalscore naar voren (AUC = 0,69; p < 0,01). Bovendien ervoeren de sociotherapeuten de BVC als gebruikersvriendelijk en weinig tijdsintensief. Conclusie De BVC heeft toegevoegde waarde voor de forensische psychiatrie. Dit geldt met name voor patiënten bij wie de primaire diagnose géén persoonlijkheidsstoornis betreft.
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It is now widely accepted that decisions made by AI systems must be explainable to their users. However, in practice, it often remains unclear how this explainability should be concretely implemented. This is especially important for nontechnical users, such as claims assessors at insurance companies, who need to understand AI system decisions and be able to explain them to customers. Think, for example, of explaining a rejected insurance claim or loan application. Although the importance of explainable AI is broadly recognized, there is often a lack of practical tools to achieve it. That’s why, in this handbook, we have combined insights from two use cases in the financial sector with findings from an extensive literature review. This has led to the identification of 30 key aspects of meaningful AI explanations. Based on these aspects, we developed a checklist to help AI developers make their systems more explainable. The checklist not only provides insight into how understandable an AI application currently is for end users, but also highlights areas for improvement.
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This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.
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Research checklist to guide students in checking whether all the steps in the research and design evaluation process are in order and that important points are not overlooked.
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Het is niet altijd even duidelijk wat het begrip 'business intelligence' precies inhoudt. BI kent namelijk een diversiteit aan definities en is vanuit verschillende organisatorische en ICT-disciplines te benaderen. Deze checklist wil deze aanpak op pragmatische maar toch intelligente wijze toepasbaar maken.
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Het is inmiddels breed geaccepteerd dat beslissingen die door AI-systemen worden genomen, uitlegbaar moeten zijn aan hun gebruikers. Toch blijft het in de praktijk vaak onduidelijk hoe die uitlegbaarheid concreet vorm moet krijgen. Vooral voor niet-technische gebruikers, zoals scha-debeoordelaars bij verzekeringsmaatschappijen, is het essentieel dat zij de beslissingen van een AI-systeem goed kunnen begrijpen én kunnen uitleggen aan klanten. Denk bijvoorbeeld aan het toelichten van een afgewezen schadeclaim of leningaanvraag. Hoewel het belang van verklaarbare AI algemeen wordt erkend, ontbreekt het vaak aan praktische handvatten om dit te realiseren. Daarom hebben we in deze handreiking inzichten samengebracht uit twee use cases binnen de financiële sector én uit een uitgebreide literatuurstudie. Hieruit zijn 30 aspecten van betekenisvolle uitleg van AI voortgekomen. Op basis van deze aspecten is een checklist ontwikkeld die AI-ontwikkelaars helpt om hun systemen beter uitlegbaar te maken. De checklist biedt niet alleen inzicht in hoeverre een AI-toe-passing op dit moment begrijpelijk is voor eindgebruikers, maar maakt ook duidelijk waar nog verbeterpunten liggen.
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This open science checklist is for researchers, trainees and students to assist them in implementing open science in their research activities. The checklist is inspired by open science principles, the Netherlands Code of Conduct for Research Integrity, the REWARD movement and the replicability crisis. It consists of 14 items divided over individual phases of the research process, except items four and five that are more generally applicable. Per item we refer to the corresponding chapter in our Open Science Research Manual and to the corresponding REWARD pillar or article(s) in the Netherlands Code of Conduct for Research Integrity. Some items focus more on quality aspects because sharing materials is most valuable if it is of high quality. Items concerning privacy and research ethics provide the boundary conditions for open science.
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Study design: A secondary analysis of a systematic review. Background: Manipulations or mobilizations are commonly used interventions in patients with mechanical neck pain. The treatment effects have often been studied in randomized controlled trials (RCT) which are generally considered the gold standard in evaluating the treatment effects, mainly due to its high internal validity. External validity is defined as the extent to which the effects can be generalised to clinical practice. An important prerequisite for this is that interventions used in clinical trials can be replicated in clinical practice. It can be questioned if interventions utilized in randomized controlled trials can be translated into clinical practice. Objectives: The overall aim of this study is to examine whether the quality of the description of manipulation and mobilization interventions is sufficient for to replication of these interventions in clinical practice. Methods: A comprehensive literature search was performed. Two independent researchers used the Template for Intervention Description and Replication (TIDieR) which is a 12-item checklist for describing the completeness of the interventions. Results: Sixty-seven articles were included that used manipulation and/or mobilization interventions for patients with mechanical neck pain. None of the articles describe the intervention e.g. all the items on the TIDieR list. Considering item 8 (a-f) of the TIDieR checklist only one article described the used techniques completely. Conclusion: Manipulation or a mobilization interventions are poorly reported in RCTs, which jeopardize the external validity of RCTs, making it difficult for clinicians and researchers to replicate these interventions.
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