Academic design research often fails to contribute to design practice. This dissertation explores how design research collaborations can provide knowledge that design professionals will use in practice. The research shows that design professionals are not addressed as an important audience between the many audiences of collaborative research projects. The research provides insight in the learning process by design professionals in design research collaborations and it identifies opportunities for even more learning. It shows that design professionals can learn about more than designing, but also about application domains or project organization.
AIM: The aim of this study is to investigate the effect of a more 'community-oriented' baccalaureate nursing curriculum on students' intervention choice in community care.BACKGROUND: Following a healthcare shift with increased chronic diseases in an ageing patient population receiving care at home, nursing education is revising its curricula with new themes (e.g., self-management) on community care. Although it seems obvious that students incorporate these themes in their nursing care interventions, this is unclear. This study investigates the effect of a redesigned curriculum on students' care intervention choice in community nursing.DESIGN: A quasi-experimental quantitative study.METHODS: This study with an historic control group (n = 328; study cohorts graduating in 2016 and 2017; response rate 83 %) and an intervention group n = 152; graduating in 2018; response rate 80 %) was performed at a University of Applied Sciences in the Netherlands. The intervention group experienced a curriculum-redesign containing five new themes related to community care (e.g., enhancing self-management, collaboration with the patients' social network, shared decision making, using health technology and care allocation). The primary outcome 'intervention choice in community nursing' was assessed with a specially developed vignette instrument 'Assessment of Intervention choice in Community Nursing' (AICN). Through multiple regression analyses we investigated the effect of the curriculum-redesign on students' intervention choice (more 'traditional' interventions versus interventions related to the five new themes). The control and intervention groups were compared on the number of interventions per theme and on the number of students choosing a theme, with a chi-square or T-test.RESULTS: Students who studied under the more community-oriented curriculum chose interventions related to the new themes significantly more often, F(1461) = 14.827, p = <0.001, R2 = .031. However, more traditional interventions are still favourite (although less in the intervention group): 74.5 % of the chosen interventions in the historic control group had no relation with the new curriculum-themes, vs. 71.3 % in the intervention group; p = .055).CONCLUSIONS: Students who experienced a more 'community-oriented' curriculum were more likely, albeit to a limited extent, to choose the new community care themes in their caregiving. Seeing this shift in choices as a step in the right direction, it can be expected that the community care field in the longer term will benefit from these better skilled graduates.
MULTIFILE
Background: The shift in healthcare to extramural leads to more patients with complex health problems receiving nursing care at home. However, the interest of baccalaureate nursing students for community nursing is moderate, which contributes to widespread labour-market shortages. This study investigates the effect of a more ‘communitycare-oriented’ curriculum on nursing students’ perceptions of community care. Methods: A quasi-experimental quantitative survey study with a historic control group (n = 477; study cohorts graduating in 2015, 2016, and 2017; response rate 90%) and an intervention group (n = 170; graduating in 2018; response rate 93%) was performed in nursing students of a University of Applied Sciences in a large city in the Netherlands. The intervention group underwent a new curriculum containing extended elements of community care. The primary outcome was assessed with the Scale on Community Care Perceptions (SCOPE). The control and intervention group were compared on demographics, placement preferences and perceptions with a chi-square or T-test. Multiple regression was used to investigate the effect of the curriculum-redesign on nursing students’ perceptions of community care.Results: The comparison between the control and intervention group on students’ perceptions of community care shows no significant differences (mean 6.18 vs 6.21 [range 1–10], respectively), nor does the curriculum-redesign have a positive effect on students’ perceptions F (1,635) = .021, p = .884, R2 = < .001. The comparison on placement preferences also shows no significant differences and confirms the hospital’s popularity (72.7% vs 76.5%, respectively) while community care is less often preferred (9.2% vs 8.2%, respectively). The demographics ‘working in community care’ and ‘belonging to a church/religious group’ appear to be significant predictors of more positive perceptions of community care. Conclusions: Graduating students who experienced a more ‘community-care-oriented’ curriculum did not more often prefer community care placement, nor did their perceptions of community care change. Apparently, four years of education and placement experiences have only little impact and students’ perceptions are relatively static. It would be worth a try to conduct a large-scale approach in combination with a carefully thought out strategy, based on and tying in with the language and culture of younger people. Keywords: Community care, Nurse education, Curriculum design, Perceptions, Career choice
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
