Since the financial and administrative liberalisation from the government in the late 1980s and the 1990s, the Dutch housing associations have been very dynamic, regarding the considerable extension of both commercial and social activities, the increased reliance and dependence on market circumstances, and the large number of amalgamations, creating bigger organisations. In recent years the Dutch social housing sector is under increased pressure as a consequence of the credit crunch, increased tax levies and the national implementation in the sector of EU regulations on ‘Services of General Economic Interest’. Factors like these are likely to have an effect on the organisational strategies of housing associations, the main providers of social housing in the Netherlands. The direction and the size of these effects, however, are not well known. A recent inquiry among housing associations sheds more light on this. In this paper, we make use of a classification including a socialcommercial dimension and a dimension between so-called ‘prospectors’ and ‘defenders’. This classification proves to be an adequate tool to describe the recent developments in the sector. It is concluded that, in general, housing associations are focussing more on traditional social housing tasks and ‘defending’ strategies, implying a shift back compared to the trend in recent decades.
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Purpose: This study aims to capture the complex clinical reasoning process during tailoring of exercise and dietary interventions to adverse effects and comorbidities of patients with ovarian cancer receiving chemotherapy. Methods: Clinical vignettes were presented to expert physical therapists (n = 4) and dietitians (n = 3). Using the think aloud method, these experts were asked to verbalize their clinical reasoning on how they would tailor the intervention to adverse effects of ovarian cancer and its treatment and comorbidities. Clinical reasoning steps were categorized in questions raised to obtain additional information; anticipated answers; and actions to be taken. Questions and actions were labeled according to the evidence-based practice model. Results: Questions to obtain additional information were frequently related to the patients’ capacities, safety or the etiology of health issues. Various hypothetical answers were proposed which led to different actions. Suggested actions by the experts included extensive monitoring of symptoms and parameters, specific adaptations to the exercise protocol and dietary-related patient education. Conclusions: Our study obtained insight into the complex process of clinical reasoning, in which a variety of patient-related variables are used to tailor interventions. This insight can be useful for description and fidelity assessment of interventions and training of healthcare professionals.
MULTIFILE
INTRODUCTION: After treatment with chemotherapy, many patients with breast cancer experience cognitive problems. While limited interventions are available to improve cognitive functioning, physical exercise showed positive effects in healthy older adults and people with mild cognitive impairment. The Physical Activity and Memory study aims to investigate the effect of physical exercise on cognitive functioning and brain measures in chemotherapy-exposed patients with breast cancer with cognitive problems.METHODS AND ANALYTICS: One hundred and eighty patients with breast cancer with cognitive problems 2-4 years after diagnosis are randomised (1:1) into an exercise intervention or a control group. The 6-month exercise intervention consists of twice a week 1-hour aerobic and strength exercises supervised by a physiotherapist and twice a week 1-hour Nordic or power walking. The control group is asked to maintain their habitual activity pattern during 6 months. The primary outcome (verbal learning) is measured at baseline and 6 months. Further measurements include online neuropsychological tests, self-reported cognitive complaints, a 3-tesla brain MRI, patient-reported outcomes (quality of life, fatigue, depression, anxiety, work performance), blood sampling and physical fitness. The MRI scans and blood sampling will be used to gain insight into underlying mechanisms. At 18 months online neuropsychological tests, self-reported cognitive complaints and patient-reported outcomes will be repeated.ETHICS AND DISSEMINATION: Study results may impact usual care if physical exercise improves cognitive functioning for breast cancer survivors.TRIAL REGISTRATION NUMBER: NTR6104.
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Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
