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Research with vulnerable groups is crucial to get their input into public policy design that will directly impact on them. However, there are many methodological and ethical challenges involved in encouraging participation from groups with a wide range of intellectual, cognitive and physical capacities while ensuring that the rights and well-being of participants are protected. Rather than exploring ethical theories, this chapter is a case study describing the practical ethical considerations that were involved in designing and holding a series of focus groups with adult health and social care service users from vulnerable cohorts. It is based on a series of focus groups which the Institute of Public Health (IPH) held with specified cohorts as part of a policy development process on adult safeguarding for the Department of Health (DOH) in Ireland. The four cohorts were people with intellectual disability, cognitive impairments, significant mental health challenges and nursing home residents. This chapter does not describe the findings of the focus groups but outlines the ethical and methodological considerations that arose in designing and conducting this research, and the practical ethical safeguards employed to mitigate risk and comply with Irish and EU General Data Protection Regulation (GDPR) legislation governing health research. It outlines the ethical issues around protecting confidentiality and using incentives to encourage participation, how individuals' capacity to give informed consent was maximized, the risk-assessment and mitigation procedures used to prevent harms arising and the measures put in place to provide follow-up emotional support to participants.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This research focuses on the challenges of establishing a better medical system that can detect and diagnose diseases earlier. Using such cutting-edge health systems, healthcare practitioners may quickly and effectively manage patients’ medical issues by providing the appropriate data at the right time about the right people. The advancement of technology has increased the usefulness of devices that routinely analyse health measurements or monitoring time-sensitive health-related data. Medical professionals and patients alike are downloading health-related mobile apps to better track and manage their health. The research evidences how Internet of Things (IoT) technology may be used to support health care.

This chapter more clearly identifies the distinction between Electronic Health Record (EHR) and Electronic Medical Record (EMR), and states their value in obtaining individual-level data. Synthetic medical records may be used as a surrogate for EHR data in order to ensure digital data privacy is maintained during the development of the LHS. Synthea is an open-source simulation tool available through GitHub.¹ Extensive descriptive analysis of synthesized data is provided as a foundation for the analysis in Chapter 7.

Despite the rapid progress and developments in education and health all over the world, gender inequality is still an important issue in many parts of the world. Girls benefit less from education opportunities than boys, and it causes gender inequality. The same situation is also valid for health. While gender inequality is still an issue even in developed countries, it is more serious in the least developed and developing countries. Therefore, there is a need to reduce gender inequality through government intervention. The aim of this study is to examine the effect of government's health and education expenditures on gender inequality in the least developed and developing countries with panel data analysis. The study covers 24 countries for the 2010–2017 period. As a result of the analysis, it has been observed that the government's health expenditures reduce gender inequality, while education expenditures increase gender inequality. This finding indicates that education expenditures of governments do not reach girls in the least developed and developing countries. However, GDP per capita is the most important factor in reducing gender inequality.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

Open data are characterized by a number of economic, environmental, technological, innovative, and social benefits. They are seen as a significant contributor to the city’s transformation into smart city. This is all the more so when the society is on the border of Society 5.0, that is, shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware, etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals (SDGs). Therefore, many agendas (sustainable development strategies, action plans) now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities toward smart cities, mapping them to the features of the Society 5.0. We believe that this trend develops a new form of society, which we refer to as “open data-driven society.” It forms a bridge from Society 4.0 to Society 5.0. This chapter attempts to identify the role of openness in promoting human-centric smart society, smart city, and smart living.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.