A keynote address, introducing the pdca quality systeem into education in prosthetics and orthotcs.
BACKGROUND: The quality standards of the Dutch Society of Intensive Care require monitoring of the satisfaction of patient's relatives with respect to care. Currently, no suitable instrument is available in the Netherlands to measure this. This study describes the development and psychometric evaluation of the questionnaire-based Consumer Quality Index 'Relatives in Intensive Care Unit' (CQI 'R-ICU'). The CQI 'R-ICU' measures the perceived quality of care from the perspective of patients' relatives, and identifies aspects of care that need improvement.METHODS: The CQI 'R-ICU' was developed using a mixed method design. Items were based on quality of care aspects from earlier studies and from focus group interviews with patients' relatives. The time period for the data collection of the psychometric evaluation was from October 2011 until July 2012. Relatives of adult intensive care patients in one university hospital and five general hospitals in the Netherlands were approached to participate. Psychometric evaluation included item analysis, inter-item analysis, and factor analysis.RESULTS: Twelve aspects were noted as being indicators of quality of care, and were subsequently selected for the questionnaire's vocabulary. The response rate of patients' relatives was 81% (n = 455). Quality of care was represented by two clusters, each showing a high reliability: 'Communication' (α = .80) and 'Participation' (α = .84). Relatives ranked the following aspects for quality of care as most important: no conflicting information, information from doctors and nurses is comprehensive, and health professionals take patients' relatives seriously. The least important care aspects were: need for contact with peers, nuisance, and contact with a spiritual counsellor. Aspects that needed the most urgent improvement (highest quality improvement scores) were: information about how relatives can contribute to the care of the patient, information about the use of meal-facilities in the hospital, and involvement in decision-making on the medical treatment of the patient.CONCLUSIONS: The CQI 'R-ICU' evaluates quality of care from the perspective of relatives of intensive care patients and provides practical information for quality assurance and improvement programs. The development and psychometric evaluation of the CQI 'R-ICU' led to a draft questionnaire, sufficient to justify further research into the reliability, validity, and the discriminative power of the questionnaire.
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De module Leren en Professioneel Handelen is onderdeel van de opleiding Master Educational Needs van het Seminarium voor Orthopedagogiek. Bij aanvang van deze module krijgen de studenten feedback op hun leerkrachtvaardigheden op basis van observatie van hun lesgedrag met het ICALT-observatie instrument. In de loop van de moduleperiode verdiepen de studenten zich in de beschikbare kennis over effectief leergedrag en worden zij in de gelegenheid gesteld om de leerkrachtvaardigheden te oefenen die nog verbetering behoeven. Uit een tweede observatie, aan het eind van de module, blijkt dat gemiddeld alle studenten er in slagen hun lesgedrag betekenisvol te verbeteren. Dit geldt voor zowel studenten met veel en weinig beroepservaring, als voor studenten die werkzaam zijn op verschillende schoolsoorten. We concluderen op basis van deze bevindingen, dat de bij het Seminarium voor Orthopedagogiek gehanteerde werkwijze uiterst effectief is.
In dit project zal een online onderwijsmodule worden ontworpen. In deze module zal een deel van de output van het project Bouwen met Levende Natuur worden verwerkt tot onderwijs. Het maken van online course materiaal binnen de HZ onderwijsonwikkeling, waar zowel echte casuistiek uit de de beroepspraktijk, als gebruik van ICT mogelijkheden centraal staan. Door de modulaire opbouw zal het mogelijk zijn onderdelen in verschillende courses te verwerken. De docent kan dan de module naar eigen wens, en onafhankelijk van de beschikbaarheid van interne of externe gastdocenten, inzetten voor ‘blended learning’. De benadering binnen de learning unit(s) volgt het constructivisme, activiteiten die te maken hebben met kennisoverdracht, zullen derhalve worden afgewisseld met verwerkingsopdrachten. De volledige onderwijsmodule richt zich vooral op onderwijs op het gebied van Coastal Engineering van de opleiding Civiele Techniek (CT), in eerste instantie van de Delta Academy; CT studenten blijken behoefte te hebben aan een uitleg van ecologische principes vanuit vanuit een meer technisch perspectief. De learning units/onderwijsmodule is uiteraard ook beschikbaar voor andere hbo opleidingen. Het geselecteerde gedeelte, de eerste learning unit, zal ook bruikbaar zijn voor de course Integrated Coastal Zone Management (ICZM), waarin oa het concept Building with Nature wordt uitgelegd. In de huidige vorm wordt dit onderdeel op de klassieke manier gebracht, in de vorm van een hoorcollege. De ontwikkeling van online materiaal maakt de afwisseling met het verwerken van de aangebrachte kennis eenvoudiger; de structuur daarvoor wordt in de online versie al aangebracht. Deze learning unit brengt niet alleen wat aanvullende benaderingen vanuit technisch perspectief, maar is ook een aanpassing, die het geheel hestructureert volgens het constructivisme. De course ICZM is een keuze-course, bedoeld voor Aquatische Ecotechnologie (AET), Delta Management (DM) en CT studenten; waar CT studenten meer behoefte hebben aan een technisch perspectief, heeft deze course ook te maken met DM studenten, die juist wat meer kennis zouden moeten maken met meer technische benaderingen.
In this project, the AGM R&D team developed and refined the use of a facial scanning rig. The rig is a physical device comprising multiple cameras and lighting that are mounted on scaffolding around a 'scanning volume'. This is an area at which objects are placed before being photographed from multiple angles. The object is typically a person's head, but it can be anything of this approximate size. Software compares the photographs to create a digital 3D recreation - this process is called photogrammetry. The 3D model is then processed by further pieces of software and eventually becomes a face that can be animated inside in Unreal Engine, which is a popular piece of game development software made by the company Epic. This project was funded by Epic's 'Megagrant' system, and the focus of the work is on streamlining and automating the processing pipeline, and on improving the quality of the resulting output. Additional work has been done on skin shaders (simulating the quality of real skin in a digital form) and the use of AI to re/create lifelike hair styles. The R&D work has produced significant savings in regards to the processing time and the quality of facial scans, has produced a system that has benefitted the educational offering of BUas, and has attracted collaborators from the commercial entertainment/simulation industries. This work complements and extends previous work done on the VIBE project, where the focus was on creating lifelike human avatars for the medical industry.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
