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Coalmining was a notoriously dangerous industry and many of its workers experienced injury and disease. However, the experiences of the many disabled people within Britain's most dangerous industry have gone largely unrecognised by historians. This book examines the British coal industry through the lens of disability, using an interdisciplinary approach to examine the lives of disabled miners and their families.The book considers the coal industry at a time when it was one of Britain's most important industries, and follows it through a period of growth up to the First World War, through strikes, depression and wartime, and into an era of decline. During this time, the statutory provision for disabled people changed considerably, most notably with the first programme of state compensation for workplace injury. And yet disabled people remained a constant presence in the industry as many disabled miners continued their jobs or took up 'light work'. The burgeoning coalfields literature used images of disability on a frequent basis and disabled characters were used to represent the human toll of the industry.A diverse range of sources are used to examine the economic, social, political and cultural impact of disability in the coal industry, looking beyond formal coal company and union records to include autobiographies, novels and oral testimony. It argues that, far from being excluded entirely from British industry, disability and disabled people were central to its development. The book will appeal to students and academics interested in disability history, disability studies, social and cultural history, and representations of disability in literature.

"This book provides a forum for the cybernetics field in critical emerging technologies, including research into design, engineering, and technological aspects of cyborg creation and existence alongside humankind for issues in their potential acceptance, participation, policy, governance, and requisite socialization between individualization and corporate, global, networked, mechanized human and humanized machine experiences"--

Abstract
In this paper, I discuss some of the wider uses of adaptive and network sampling designs. Three uses of sampling designs are to select units from a population to make inferences about population values, to select units to use in an experiment, and to distribute interventions to benefit a population. The most useful approaches for inference from adaptively selected samples are design-based methods and Bayesian methods. Adaptive link-tracing network sampling methods are important for sampling populations that are otherwise hard to reach. Sampling in changing populations involves temporal network or spatial sampling design processes with units selected both into and out of the sample over time. Averaging or smoothing fast-moving versions of these designs provides simple estimates of network-related characteristics. The effectiveness of intervention programs to benefit populations depends a great deal on the sampling and assignment designs used in spreading the intervention.

Few industries are buffeted from as many strong forces as healthcare. The industry is highly regulated, thus dramatically increasing costs and sometimes even interfering with the ability to deliver healthcare. New drugs, treatments, and medical technologies are so common that keeping track of them can be overwhelming, and incorporating them into patient care or administration can be costly and complicated. On the social side, different groups have different opinions on any given topic and often the right thing to do depends on your point of view. Third party payers add another level of complexity, and competition adds yet another layer of difficulty as organizations seek to grow patient volume by positioning themselves as distinguished in terms of cost, quality, accessibility, and quality of patient experience.

Information technology (IT) requires a significant investment, involving up to 10.5% of revenue for some firms. Managers responsible for aligning IT investments with their firm's strategy seek to minimize technology costs, while ensuring that the IT infrastructure can accommodate increasing utilization, new software applications, and modifications to existing software applications. It becomes more challenging to align IT infrastructure and IT investments with firm strategy when firms operate in multiple geographic markets, because the firm faces different competitive positions and unique challenges in each market. We discussed these challenges with IT executives at four Forbes Global 2000 firms headquartered in Northern Europe. We build on interviews with these executives to develop a discrete-time, finite-horizon Markov decision model to identify the most economically-beneficial IT infrastructure configuration from a set of alternatives. While more flexibility is always better (all else equal) and lower cost is always better (all else equal), our model helps firms evaluate the tradeoff between flexibility and cost given their business strategy and corporate structure. Our model supports firms in the decision process by incorporating their data and allowing firms to include their expectations of how future business conditions may impact the need to make IT changes. Because the model is flexible enough to accept parameters across a range of business strategies and corporate structures, the model can help inform decisions and ensure that design choices are consistent with firm strategy.

In an effort to reduce cost and improve quality, health care payers have enacted a number of incentives to motivate providers to focus their efforts on achieving better clinical outcomes and reducing the prevalence and progression of disease. In response to these incentives, providers are entering into new arrangements such as accountable care organizations and patient-centered medical homes to redesign delivery processes and achieve quality and cost objectives. This article reports the results of a study designed to evaluate the impact on cost and quality of care resulting from services provided by Health Diagnostic Laboratory, Inc., a clinical laboratory with a comprehensive care model. The results show that patients who utilized these laboratory services experienced lower total cost of care (23% reduction, P < 0.01) and improved lipid profiles during the follow-up period. Total cost reductions were related to cost reductions found in both inpatient and ambulatory care. These findings suggest that accountable care organizations, patient-centered medical homes, and other groups entering shared savings initiatives should consider the potential role ancillary service providers with comprehensive care models can play in the delivery of integrated care. (Population Health Management 2014; 17: 121-126)

"The Third Edition retains the general organization of the prior two editions, but it incorporates new material throughout the text. The book is organized into six parts: Part I covers basic sampling from simple random sampling to unequal probability sampling; Part II treats the use of auxiliary data with ratio and regression estimation and looks at the ideas of sufficient data, model, and design in practical sampling; Part III covers major useful designs such as stratified, cluster and systematic, multistage, and double and network sampling; Part IV examines detectability methods for elusive populations, and basic problems in detectability, visibility, and catchability are discussed; Part V concerns spatial sampling with the prediction methods of geostatistics, considerations of efficient spatial designs, and comparisons of different observational methods including plot shapes and detection aspects; and Part VI introduces adaptive sampling designs in which the sampling procedure depends on what is observed during the survey. For this new edition, the author has focused on thoroughly updating the book with a special emphasis on the first 14 chapters since these topics are invariably covered in basic sampling courses. The author has also implemented new approaches to explain the various techniques in the book, and as a result, new examples and explanations have been added throughout. In an effort to improve the presentation and visualization of the book, new figures as well as replacement figures for previously existing figures have been added. This book has continuously stood out from other sampling texts since the figures evoke the idea of each sampling design. The new figures will help readers to better visualize and understand the underlying concepts such as the different sampling strategies"--

Many hospitals face the problem of insufficient capacity to meet demand for inpatient beds, especially during demand surges. This results in quality degradation of patient care due to large delays from admission time to the hospital until arrival at a floor. In addition, there is loss of revenue because of the inability to provide service to potential patients. A solution to the problem is to proactively transfer patients between floors in anticipation of a demand surge. Optimal reallocation poses an extraordinarily complex problem that can be modeled as a finite-horizon Markov decision process. Based on the optimization model, a decision-support system has been developed and implemented at Windham Hospital in Willimantic, Connecticut. Projections from an initial trial period indicate very significant financial gains of about 1% of their total revenue, with no negative impact on any standard quality of care or staffing effectiveness indicators. In addition, the hospital showed a marked improvement in quality of care because of a resulting decrease of almost 50% in the average time that an admitted patient has to wait from admission until being transferred to a floor.