As migrant populations age, the care system is confronted with the question how to respond to care needs of an increasingly diverse population of older adults. We used qualitative intersectional analysis to examine differential preferences and experiences with care at the end of life of twenty-five patients and their relatives from Suriname, Morocco and Turkey living in The Netherlands. Our analysis focused on the question how–in light of impairment–ethnicity, religion and gender intersect to create differences in social position that shape preferences and experiences related to three main themes: place of care at the end of life; discussing prognosis, advance care, and end-of-life care; and, end-of-life decision-making. Our findings show that belonging to an ethnic or religious minority brings forth concerns about responsive care. In the nursing home, patients’ minority position and the interplay thereof with gender make it difficult for female patients to request and receive responsive care. Patients with a strong religious affiliation prefer to discuss diagnosis but not prognosis. These preferences are at interplay with factors related to socioeconomic status. The oversight of this variance hampers responsive care for patients and relatives. Preferences for discussion of medical aspects of care are subject to functional impairment and faith. Personal values and goals often remain unexpressed. Lastly, preferences regarding medical end-of-life decisions are foremost subject to religious affiliation and associated moral values. Respondents’ impairment and limited Dutch language proficiency requires their children to be involved in decision-making. Intersecting gendered care roles determine that mostly daughters are involved. Considering the interplay of aspects of social identity and their effect on social positioning, and pro-active enquiry into values, goals and preferences for end-of-life care of patients and their relatives are paramount to achieve person centred and family-oriented care responsive to the needs of diverse communities.
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The origins of SWOT analysis have been enigmatic, until now. With archival research, interviews with experts and a review of the available literature, this paper reconstructs the original SOFT/SWOT approach, and draws potential implications. During a firm's planning process, all managers are asked to write down 8 to 10 key planning issues faced by their units. Each manager grades, with evidence, these issues as either safeguarding the Satisfactory; opening Opportunities; fixing Faults; or thwarting Threats: hence SOFT (which is later merely relabeled to Strengths, Weaknesses, Opportunities and Threats, or SWOT). Subgroups of managers have several dialogues about these issues with the instruction to include the needs and expectations of all the firm's stakeholders. Their developed resolutions or proposals become input for the executive planning committee to articulate corporate purpose(s) and strategies. SWOT's originator, Robert Franklin Stewart, emphasized the crucial role that creativity plays in the planning process. The SOFT/SWOT approach curbs mere top-down strategy making to the benefit of strategy alignment and implementation; Introducing digital means to parts of SWOT's original participative, long-range planning process, as suggested herein, could boost the effectiveness of organizational strategizing, communication and learning. Archival research into the deployment of SOFT/SWOT in practice is needed.
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.
