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There has been immense pressure exerted by healthcare service consumers and other stakeholders for trusts to purchase, manage and deliver care service at the optimum cost. Many are searching for sustainable strategic options which would proactively redesign, revitalise and manage the effectiveness of their clinical and non‐clinical services. This study evaluates how a facilities management partnering arrangement in the National Healthcare Services (NHS) trust could lead to cost savings and improved customer focused service provision in NHS trust hospitals. It investigates how a modern day approach of managing non‐core related services under one umbrella, facilities management, has brought about innovation, competitiveness, and entrepreneurial skills to reduce some of the escalating costs in the Derbyshire Royal Infirmary. Concludes that facilities management partnering arrangements can secure added value to the healthcare sector and therefore enhance service quality and improve the corporate image.

The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.

In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.

First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.

Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.

Research on health care disparities is making important descriptive and analytical strides, and the issue of disparities has gained the attention of policymakers in the United States, other nation-states, and international organizations. Still, disparities research scholarship remains US-centric and too rarely takes a cross-national comparative approach to answering its questions. The US-centricity of disparities research has fostered a fixation on race and ethnicity that, although essential to understanding health disparities in the United States, has truncated the range of questions that researchers investigate. In this chapter, we make a case for comparative research that highlights its ability to identify the institutional factors that may affect disparities.

We discuss the central methodological challenges to comparative research. After describing current solutions to such problems, we use data from the World Values Survey to show the impact of key social fault lines on self-assessed health in Europe and the United States.

The negative impact of socioeconomic status (SES) on health is more generalizable across context, than the impact of race/ethnicity or gender.

Our analysis includes a limited number of countries and relies on one measure of health.

The chapter represents a first step in a research agenda to understand health inequalities within and across societies.

There is an on-going debate as to whether health is negatively affected by economic inequality. Still, we have limited knowledge of the mechanisms relating inequality to individual health and very little evidence comes from less-developed economies. We use individual and multi-level data from Zambia on child nutritional health to test three hypotheses consistent with a negative correlation between income inequality and population health: the absolute income hypothesis (AIH), the relative income hypothesis (RIH) and the income inequality hypothesis (IIH). The results confirm that absolute income positively affects health. For the RIH we find sensitivity to the reference group used. Most interestingly, we find higher income inequality to robustly associate with better child health. The same pattern appears in a cross country regression. To explain the conflicting results in the literature we suggest examining potential mediators such as generosity, food sharing, trust and purchasing power.

This chapter explores how data-driven methods such as Artificial Intelligence pose real concerns for individual privacy. The current paradigm of collecting data from those using online applications and services is reinforced by significant potential profits that the private sector stands to realize by delivering a broad range of services to users faster and more conveniently. Terms of use and privacy agreements are a common source of confusion, and are written in a way that dulls their impact and dopes most into automatically accepting a certain level of risk in exchange for convenience and “free” access. Third parties, including the government, gain access to these data in numerous ways. If the erosion of individual protections of privacy and the potential dangers this poses to our autonomy and democratic ideals were not alarming enough, the digital surrogate product of “you” that is created from this paradigm might one day freely share thoughts, buying habits, and your pattern of life with whoever owns these data. We use an ethical framework to assess key factors in these issues and discuss some of the dilemmas posed by Artificial Intelligence methods, the current norm of sharing one's data, and what can be done to remind individuals to value privacy. Will our digital surrogate one day need protections too?

Persistent change has placed considerable pressure on organizations to keep up or fade into obscurity. Firms that remain viable, or even thrive, are staffed with decision-makers who capably steer organizations toward opportunities and away from threats. Accordingly, leadership development has never been more critical. In this chapter, the authors propose that leader development is an inherently dyadic process initiated to communicate formal and informal expectations. The authors focus on the informal component, in the form of organizational politics, as an element of leadership that is critical to employee and company success. The authors advocate that superiors represent the most salient information source for leader development, especially as it relates to political dynamics embedded in work systems. The authors discuss research associated with our conceptualization of dyadic political leader development (DPLD). Specifically, the authors develop DPLD by exploring its conceptual underpinnings as they relate to sensemaking, identity, and social learning theories. Once established, the authors provide a refined discussion of the construct, illustrating its scholarly mechanisms that better explain leader development processes and outcomes. The authors then expand research in the areas of political skill, political will, political knowledge, and political phronesis by embedding our conceptualization of DPLD into a political leadership model. The authors conclude by discussing methodological issues and avenues of future research stemming from the development of DPLD.

Because of the 2008 global financial crisis aftermaths, economic downturn and prolonged recession, several OECD countries have adopted an austerity compound by significantly reducing public health expenditure (PHE) for dealing with their fiscal pressure and sovereign-debt challenges. Against this backdrop, this study aims to examine the responsiveness of PHE to macro-fiscal determinants, demography, as well to private health insurance (PHI) financing.

The authors gather annual panel data from four international organizations databases for the total of OECD countries from a period lasting from 2000 to 2017. The authors apply static and dynamic econometric methodology to deal with panel data and assess the impact of several parameters on PHE.

The authors’ findings indicate that gross domestic product, fiscal capacity, tax revenues and population aging have a positive effect on PHE. Further, the authors find that both unemployment rate and voluntary private health insurance financing present a negative statistically significant impact on our estimated outcome variable. Different specifications and sample periods applied in the regression models reveal how inseparably associated are PHE and OECD's economies compliance on macro-fiscal policies for offsetting public finances derailment.

Providing more evidence on the responsiveness of PHE to several macro-fiscal drivers, it can be a helpful tool for governments to reconsider their persistence on fiscal adjustments measures and rank public health financing to the top of their political agenda. Health systems policies for meeting Universal Health Coverage (UHC) objectives, they should also take into consideration the voluntary PHI institution, especially for economies with insufficient fiscal capacity to raise public health financing.

To the best of knowledge, the impact of unemployment and voluntary PHI funding on public health financing, apart from other macro-fiscal and demographical parameters effect, remains unnoticed in the existing published studies on the topic.

Communication aids sports teams in achieving physical feats through the process of teamwork. Working in fast-paced, high-pressure environments, sports teams are epitomized by their focus on effective physical and mental coordination, constantly adapting as a team to changing information and dynamic opponents. However, successful physical and mental coordination in sports teams are dependent on communication that may occur well before gameplay. For this reason, coaches and team leaders focus on various communicative activities throughout the life of a sports team, such as knowledge sharing, role clarity, goal setting, motivation, culture, and cohesion. Sports teams also provide unique insights into how teams communicate in single-gender groups, how heightened emotion affects team performance, and how event finality plays a role in team process.

Multiple connotations and conceptions of health need are currently in use. The purpose of this paper is to specify some important distinctions regarding this confusing multiplicity in a taxonomic fashion relevant to the economic problems that arise in addressing health need. Classification is possible with the relevant concepts in conventional economic theory. The classification applies wherever economic considerations bear upon health need.

Initially, some seminal economic ideas about need are presented from Marshall, Pauly, Banfield, Jevons, Deaton and Meullbauer, and Georgescu‐Roegen. Recent discussions of basic needs by Sen and Nussbaum concerning “capabilities” and human flourishing are also considered. Ruger's subsequent developments of these concepts specifically for health are noted. The paper then specifies and classifies the current economic connotations of “health need” by applying positive economic analysis and the framework of economic theory. In particular, the conventional theories of consumer demand and production supply are useful. Geometric tools of analysis along with illustrations from the health sector specify various distinctions and classifications.

The uses of the generic term “need” relate to quite different economic problems. The findings show how diverse interpretations of need can be specified.

Distinctions over health need are important since, in many Western countries, need is one of the “pillars” of the Welfare State. Effective policy requires sound conceptions and measurements of need. Given the relevance of economics for approaching competing resource uses in the face of health need, measurement of need is improved with taxonomy, and confusion reduced.