Ascertaining the contribution of research is complex; this is not a conclusion but a starting point for the preliminary thoughts in this inaugural lecture. The guiding question is: where does this complexity lie? The dominant answer that has taken root in many practices flattens this complexity into a line. As a handle, or a rule of thumb. The concept of continuous effects serves as a crowbar to break open this one-dimensionality, not least to do more justice to practice-based research at universities of applied sciences. This allows for a different way of looking at how practice-based research contributes to change: from continuous effects ‘stretching each moment to the fullest’ and indicators of the effects of direct interactions, to multiple action perspectives beyond merely generating new knowledge to bring about change.
Background: In 2009, the Steering Committee for Pregnancy and Childbirth in the Netherlands recommended the implementation of continuous care during labor in order to improve perinatal outcomes. However, in current care, routine maternity caregivers are unable to provide this type of care, resulting in an implementation rate of less than 30%. Maternity care assistants (MCAs), who already play a nursing role in low risk births in the second stage of labor and in homecare during the postnatal period, might be able to fill this gap. In this study, we aim to explore the (cost) effectiveness of adding MCAs to routine first- and second-line maternity care, with the idea that these MCAs would offer continuous care to women during labor. Methods: A randomized controlled trial (RCT) will be performed comparing continuous care (CC) with care-as-usual (CAU). All women intending to have a vaginal birth, who have an understanding of the Dutch language and are > 18 years of age, will be eligible for inclusion. The intervention consists of the provision of continuous care by a trained MCA from the moment the supervising maternity caregiver establishes that labor has started. The primary outcome will be use of epidural analgesia (EA). Our secondary outcomes will be referrals from primary care to secondary care, caesarean delivery, instrumental delivery, adverse outcomes associated with epidural (fever, augmentation of labor, prolonged labor, postpartum hemorrhage, duration of postpartum stay in hospital for mother and/or newborn), women’s satisfaction with the birth experience, cost-effectiveness, and a budget impact analysis. Cost effectiveness will be calculated by QALY per prevented EA based on the utility index from the EQ-5D and the usage of healthcare services. A standardized sensitivity analysis will be carried out to quantify the outcome in addition to a budget impact analysis. In order to show a reduction from 25 to 17% in the primary outcome (alpha 0.05 and bèta 0.20), taking into account an extra 10% sample size for multi-level analysis and an attrition rate of 10%, 2 × 496 women will be needed (n = 992). Discussion: We expect that adding MCAs to the routine maternity care team will result in a decrease in the use of epidural analgesia and subsequent costs without a reduction in patient satisfaction. It will therefore be a costeffective intervention. Trial registration: Trial Registration: Netherlands Trial Register, NL8065. Registered 3 October 2019 - Retrospectively registered.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
In dit project wordt gekeken naar de validatie van een procesverbeteringstool.
In dit project wordt gekeken naar de validatie van een procesverbeteringstool.Doel De belastingdienst heeft een tool ontwikkeld met als doel de samenhang tussen Business en IT te verbeteren en een proces van continu verbeteren op te starten. Deze tool wil de belastingdienst objectief te evalueren. Resultaten Dit project leidt tot de volgende resultaten: Een validatierapport Een verbeterplan Een wetenschappelijk artikel Looptijd 01 oktober 2021 - 31 december 2022 Aanpak Binnen dit onderzoek wordt gebruik gemaakt van de volgende onderzoeksmethoden: Focusgroepen Literatuuronderzoek Workshops Meedoen in dit onderzoek? Als je onderzoek wil doen naar tools in het werkveld van Agile werken, Business IT alignement of continu verbeteren, neem dan contact op met Paul Morsch.
