Aims: To evaluate the effects of the implementation of a professional practice model based on Magnet principles on the nurse work environment in a Dutch teaching hospital. Design: A quasi-experimental study. Methods: Data were collected from registered nurses working on the clinical wards and outpatient clinics of the hospital in June/July 2016 (baseline) and in June/September 2019 (measurement of effects). Participants completed the Dutch Essentials of Magnetism II survey, which was used to measure their perception of their work environment. After baseline measurements were collected, interventions based on a professional practice model incorporating Magnet principles were implemented to improve the nurse work environment. Descriptive statistics and independent t-tests were conducted to examine differences between survey outcomes in 2016 and 2019. Results: Survey outcomes revealed significant changes in the nurse work environment between 2016 and 2019. Seven of the eight subscales (essentials of magnetism) improved significantly. Score for overall job satisfaction increased from 7.3 to 8.0 and score for quality of care increased from 7.0 to 7.6. On unit level, 17 of the 19 units showed improvement in the nurse work environment. Conclusion: The implementation of a professional practice model positively affects the nurse work environment, job satisfaction and quality of care. Impact: Nowadays, the quality of care is threatened by workload pressure and the low autonomy experienced by nurses. Considering the global shortage of nurses and growing complexity of healthcare, it is important to invest in improving the nurse work environment. The Magnet concept created a work environment in which nurses can deliver optimal quality of care. Knowledge of how Magnet principles affect the nurse work environment in the Netherlands is missing. These study results, including the description of how the interventions were implemented, will assist other hospitals to develop improvement strategies by focusing on the nurse work environment.
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Het lectoraat Applied Quantum Computing is een samenwerking tussen de Hogeschool van Amsterdam en het Centrum Wiskunde en Informatica. Dit lectoraat gaat zich bezig houden met het leggen van een verbinding tussen enerzijds fundamenteel onderzoek en anderzijds praktische problemen. In een samenwerking met IBM, Capgemini en Qusoft zullen cases en experimenten worden uitgevoerd hoe Quantum Computing bedrijven gaat beïnvloeden. Op het gebied van Quantum Communication zal onderzocht worden hoe m.b.v. Quantum Technologie gekomen kan worden tot een veilige communicatie. Ook zal aangesloten worden bij onderzoek naar en onderwijs worden ontwikkeld rondom hoe quantum mechanische effecten praktisch ingezet kunnen worden om metingen te verrichten. Onderzoek zal verricht worden naar het implementeren van theoretische oplossingen als bedacht in de laboratoria van universiteiten voor problemen bij bedrijven en instellingen. Binnen de Hogeschool van Amsterdam zal aansluiting worden gezocht met het onderzoek dat wordt gedaan binnen diverse lectoraten van de Faculteit DMCI, zoals responsible IT (i.o) en Urban Analytics en met de onderzoekers van de groep Urban Technology van de faculteit Techniek. In het onderwijs wordt een relatie bestendigd met opleidingen als HBO-ICT, waarvoor een minor wordt ontwikkeld, en Technische Natuurkunde. Daarbuiten zal verder gewerkt worden aan een netwerk om te komen tot een ecosysteem van instellingen en bedrijven. De Hogeschool van Amsterdam draagt Marten Teitsma als lector voor. Marten Teitsma heeft heeft veel ervaring in het onderwijs, ontwikkeling daarvan, als leidinggevende en is gepromoveerd in de Artificiële Intelligentie. Binnen de hogeschool heeft hij het initiatief genomen tot diverse activiteiten op het gebied van Quantum Computing.
Every year the police are confronted with an ever increasing number of complex cases involving missing persons. About 100 people are reported missing every year in the Netherlands, of which, an unknown number become victims of crime, and presumed buried in clandestine graves. Similarly, according to NWVA, several dead animals are also often buried illegally in clandestine graves in farm lands, which may result in the spread of diseases that have significant consequences to other animals and humans in general. Forensic investigators from both the national police (NP) and NWVA are often confronted with a dilemma: speed versus carefulness and precision. However, the current forensic investigation process of identifying and localizing clandestine graves are often labor intensive, time consuming and employ classical techniques, such as walking sticks and dogs (Police), which are not effective. Therefore, there is an urgent request from the forensic investigators to develop a new method to detect and localize clandestine graves quickly, efficiently and effectively. In this project, together with practitioners, knowledge institutes, SMEs and Field labs, practical research will be carried out to devise a new forensic investigation process to identify clandestine graves using an autonomous Crime Scene Investigative (CSI) drone. The new work process will exploit the newly adopted EU-wide drone regulation that relaxes a number of previously imposed flight restrictions. Moreover, it will effectively optimize the available drone and perception technologies in order to achieve the desired functionality, performance and operational safety in detecting/localizing clandestine graves autonomously. The proposed method will be demonstrated and validated in practical operational environments. This project will also make a demonstrable contribution to the renewal of higher professional education. The police and NVWA will be equipped with operating procedures, legislative knowledge, skills and technological expertise needed to effectively and efficiently performed their forensic investigations.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
