In this paper, we focus on how the qualitative vocabulary of Dynalearn, which is used for describing dynamic systems, corresponds to the mathematical equations used in quantitative modeling. Then, we demonstrate the translation of a qualitative model into a quantitative model, using the example of an object falling with air resistance.
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Art therapy is widely used and effective in the treatment of patients diagnosed with Personality Disorders (PDs). Current psychotherapeutic approaches may benefit from this additional therapy to improve their efficacy. But what is the patient perspective upon this therapy? This study explored perceived benefits of art therapy for patients with PDs to let the valuable perspective of patients be taken into account. Using a quantitative survey study over 3 months (N = 528), GLM repeated measures and overall hierarchical regression analyses showed that the majority of the patients reported quite a lot of benefit from art therapy (mean 3.70 on a 5-point Likert scale), primarily in emotional and social functioning. The improvements are concentrated in specific target goals of which the five highest scoring goals affected were: expression of emotions, improved (more stable/positive) self-image, making own choices/autonomy, recognition of, insight in, and changing of personal patterns of feelings, behaviors and thoughts and dealing with own limitations and/or vulnerability. Patients made it clear that they perceived these target areas as having been affected by art therapy and said so at both moments in time, with a higher score after 3 months. The extent of the perceived benefits is highly dependent for patients on factors such as a non-judgmental attitude on the part of the therapist, feeling that they are taken seriously, being given sufficient freedom of expression but at the same time being offered sufficient structure and an adequate basis. Age, gender, and diagnosis cluster did not predict the magnitude of perceived benefits. Art therapy provides equal advantages to a broad target group, and so this form of therapy can be broadly indicated. The experienced benefits and the increase over time was primarily associated with the degree to which patients perceive that they can give meaningful expression to feelings in their artwork. This provides an indication for the extent of the benefits a person can experience and can also serve as a clear guiding principle for interventions by the art therapist.
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The main goal of this study was to investigate if a computational analyses of text data from the National Student Survey (NSS) can add value to the existing, manual analysis. The results showed the computational analysis of the texts from the open questions of the NSS contain information which enriches the results of standard quantitative analysis of the NSS.
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Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
DISTENDER will provide integrated strategies by building a methodological framework that guide the integration of climate change(CC) adaptation and mitigation strategies through participatory approaches in ways that respond to the impacts and risks of climatechange (CC), supported by quantitative and qualitative analysis that facilitates the understanding of interactions, synergies and tradeoffs.Holistic approaches to mitigation and adaptation must be tailored to the context-specific situation and this requires a flexibleand participatory planning process to ensure legitimate and salient action, carried out by all important stakeholders. DISTENDER willdevelop a set of multi-driver qualitative and quantitative socio-economic-climate scenarios through a facilitated participatory processthat integrates bottom-up knowledge and locally-relevant drivers with top-down information from the global European SharedSocioeconomic Pathways (SSPs) and downscaled Representative Concentration Pathways (RCPs) from IPCC. A cross-sectorial andmulti-scale impact assessment modelling toolkit will be developed to analyse the complex interactions over multiple sectors,including an economic evaluation framework. The economic impact of the different efforts will be analyse, including damage claimsettlement and how do sectoral activity patterns change under various scenarios considering indirect and cascading effects. It is aninnovative project combining three key concepts: cross-scale, integration/harmonization and robustness checking. DISTENDER willfollow a pragmatic approach applying methodologies and toolkits across a range of European case studies (six core case studies andfive followers) that reflect a cross-section of the challenges posed by CC adaptation and mitigation. The knowledge generated byDISTENDER will be offered by a Decision Support System (DSS) which will include guidelines, manuals, easy-to-use tools andexperiences from the application of the cases studies.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
