Depression is a highly prevalent and seriously impairing disorder. Evidence suggests that music therapy can decrease depression, though the music therapy that is offered is often not clearly described in studies. The purpose of this study was to develop an improvisational music therapy intervention based on insights from theory, evidence and clinical practice for young adults with depressive symptoms. The Intervention Mapping method was used and resulted in (1) a model to explain how emotion dysregulation may affect depressive symptoms using the Component Process Model (CPM) as a theoretical framework; (2) a model to clarify as to how improvisational music therapy may change depressive symptoms using synchronisation and emotional resonance; (3) a prototype Emotion-regulating Improvisational Music Therapy for Preventing Depressive symptoms (EIMT-PD); (4) a ten-session improvisational music therapy manual aimed at improving emotion regulation and reducing depressive symptoms; (5) a program implementation plan; and (6) a summary of a multiple baseline study protocol to evaluate the effectiveness and principles of EIMT-PD. EIMT-PD, using synchronisation and emotional resonance may be a promising music therapy to improve emotion regulation and, in line with our expectations, reduce depressive symptoms. More research is needed to assess its effectiveness and principles.
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Adaptive governance describes the purposeful collective actions to resist, adapt, or transform when faced with shocks. As governments are reluctant to intervene in informal settlements, community based organisations (CBOs) self-organize and take he lead. This study explores under what conditions CBOs in Mathare informal settlement, Nairobi initiate and sustain resilience activities during Covid-19. Study findings show that CBOs engage in multiple resilience activities, varying from maladaptive and unsustainable to adaptive, and transformative. Two conditions enable CBOs to initiate resilience activities: bonding within the community and coordination with other actors. To sustain these activities over 2.5 years of Covid-19, CBOs also require leadership, resources, organisational capacity, and network capacity. The same conditions appear to enable CBOs to engage in transformative activities. How-ever, CBOs cannot transform urban systems on their own. An additional condition, not met in Mathare, is that governments, NGOs, and donor agencies facilitate, support, and build community capacities. This is the peer reviewed version of the following article: Adaptive governance by community-based organisations: Community resilience initiatives during Covid‐19 in Mathare, Nairobi. which has been published in final form at doi/10.1002/sd.2682. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
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This chapter provides insights into the complex and adaptive nature of systems and illustrates key characteristics of such systems. These contribute to an understanding of the challenges in health promotion and imply a need for more context-specific research to evaluate the health promotion interventions. CARA can address this need as it can be used to evaluate and support change in complex adaptive systems. To support and inspire other health promotion researchers who want to adopt CARA as their research approach, we have discussed our experiences and provided some guiding principles. Overall, complexity thinking can help to understand the challenges in health promotion, whereby CARA provides a possible strategy for health promotion researchers when dealing with the challenges of evaluating health promotion interventions in complex adaptive systems.
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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.
Het probleem dat deze projectaanvraag adresseert is de hoge werkdruk van zorgprofessionals in de dementiezorg. Door een stijging in het aantal ouderen met dementie, stijgt de zorgvraag, terwijl het tekort aan zorgprofessionals groeit. Door de inzet van slimme technologische innovaties zoals een Intelligente Zorgomgeving kan deze werkdruk sterk verminderd worden. Een Intelligente Zorgomgeving maakt gebruik van sensortechnieken en gebruikt Artificiële Intelligentie (AI) om gepersonaliseerde zorg te leveren door de zorgbehoefte in kaart te brengen en daarop te reageren. De Intelligente Zorgomgeving werkt daarbij samen met de zorgprofessional. Deze oplossingsrichting wordt in dit project verder uitgewerkt samen met vier zorgpartijen en drie innovatieve MKB. Aan de hand van de casus “Ondersteuning bij eten en drinken” worden Just-in-time adaptive interventions (JITAI) ontwikkeld zodat de zorgprofessional de zorgprofessional ondersteund wordt in het uitvoeren van bepaalde zorgtaken. Een voorbeeld van een interventie is het op het juiste moment geven van op de persoon aangepaste zintuigelijke prikkels (geluiden, lichten en projecties) die senioren stimuleren om te eten. Door dergelijke interventies wordt de druk op de zorgprofessional verminderd en neemt de kwaliteit van de zorg toe. Niet alleen de integratie van de AI-modules is van belang maar ook hoe de AI ‘getoond’ wordt aan de zorgprofessional. Daarom wordt er in dit project ook extra aandacht besteed aan de interactie tussen zorgprofessional en de Intelligente Zorgomgeving waardoor het gebruiksgemak wordt verhoogd en zowel cliënt als zorgprofessional een hogere mate van autonomie kunnen ervaren. Door het prototype van de Intelligente Zorgomgeving verder te ontwikkelen in zorginstellingen in samenwerking met verschillende zorgprofessionals en aandacht te besteden aan het ontwikkelen van AI en Interactie met het systeem kunnen de wensen en behoeften van de zorgprofessionals worden geïntegreerd in de Intelligente Zorgomgeving. Dit gebeurt in drie iteraties waarbij de drie opeenvolgende beschikbare living labs in toenemende mate complex en realistisch zijn.
