Sharon Schwartz – författare

Searching for the causes of mental disorders is as exciting as it it complex. The relationship between pathophysiology and its overt manifestations is exceedingly intricate, and often the causes of a disorder are elusive at best. This book is an invaluable resource for anyone trying to track these causes, whether they be clinical researchers, public health practitioners, or psychiatric epidemiologists-in-training. Uniting theory and practice in very clear language, it makes a wonderful contribution to both epidemiologic and psychiatric research. Rather than attempting to review the descriptive epidemiology of mental disorders, this book gives much more dynamic exposition of the thinking and techniques used to establish it.Starting out by tracing the brief history of psychiatric epidemiology, the book describes the study of risk factors as causes of mental disorders. Subsequent sections discuss approaches to investigation of biologic, genetic, or social causes and the statistical analysis of study results. The book concludes by following some of the problems involved in the search for genetic causes of mental disorders, and more complex casual relationships.

Searching for the causes of mental disorders is as exciting as it is complex. The relationship between pathophysiology and its overt manifestations is exceedingly intricate, and often the causes of a disorder are elusive at best. This book is an invaluable resource for anyone trying to track these causes, whether they be clinical researchers, public health practitioners, or psychiatric epidemiologists-in-training. Uniting theory and practice in very clear language, it makes a wonderful contribution to both epidemiologic and psychiatric research. Rather than attempting to review the descriptive epidemiology of mental disorders, this book gives a much more dynamic exposition of the thinking and techniques used to establish it.Starting out by tracing the brief history of psychiatric epidemiology, the book describes the study of risk factors as causes of mental disorders. Subsequent sections discuss approaches to investigation of biologic, genetic or social causes and the statistical analysis of study results. The book concludes by following some of the problems involved in the search for genetic causes of mental disorders, and more complex causal relationships.

An essential introduction to concepts of causation and causal inference that explores how our definitions of causes in epidemiology influence how we go about finding them and estimating their effects.A key goal of epidemiologic research is to uncover the causes of diseases and other health-related outcomes. But exactly what we mean by a "cause" and what types of phenomena qualify as causes, differ across historical periods, and is a contentious topic. The causal status of social phenomena has been, and continues to be, particularly precarious. In Causal Inference and the People's Health, Sharon Schwartz and Seth J. Prins offer both a synthesis of the dominant school of thought around causality and propose an approach that keeps causal concepts as an organizing principle without marginalizing social phenomena. The authors examine the implications of the "Causal Revolution,"--introduced in epidemiology as the Potential Outcomes framework--which initiated a paradigm shift across the social sciences. As the authors show, this shift influences the questions we ask, the methods we use, the narratives we construct about our study results, and thus the knowledge we use to fight for the people's health. The guiding principle of the Causal Revolution is simple but profound: researchers should specify if their goal is description, prediction, or causation. In other words, researchers should declare their causal goals even for observational studies. This principle not only produced important innovations, but it also reignited debates about the definition of causation, the causal status of important social constructs like class and gender, and the role of manipulation or intervention in causal inference. Interrogating these debates and embracing causal questions that identify and explain etiologic processes, Causal Inference and the People's Health offers a path forward that expands causal inference to include social forces as causes of the people's health, and therefore reinvigorates epidemiology's historical role in targeting systems and structures for change.

An essential introduction to concepts of causation and causal inference that explores how our definitions of causes in epidemiology influence how we go about finding them and estimating their effects. A key goal of epidemiologic research is to uncover the causes of diseases and other health-related outcomes. But exactly what we mean by a "e;cause"e; and what types of phenomena qualify as causes, differ across historical periods, and is a contentious topic. The causal status of social phenomena has been, and continues to be, particularly precarious. In Causal Inference and the People's Health, Sharon Schwartz and Seth J. Prins offer both a synthesis of the dominant school of thought around causality and propose an approach that keeps causal concepts as an organizing principle without marginalizing social phenomena. The authors examine the implications of the "e;Causal Revolution,"e;--introduced in epidemiology as the Potential Outcomes framework--which initiated a paradigm shift across the social sciences. As the authors show, this shift influences the questions we ask, the methods we use, the narratives we construct about our study results, and thus the knowledge we use to fight for the people's health. The guiding principle of the Causal Revolution is simple but profound: researchers should specify if their goal is description, prediction, or causation. In other words, researchers should declare their causal goals even for observational studies. This principle not only produced important innovations, but it also reignited debates about the definition of causation, the causal status of important social constructs like class and gender, and the role of manipulation or intervention in causal inference. Interrogating these debates and embracing causal questions that identify and explain etiologic processes, Causal Inference and the People's Health offers a path forward that expands causal inference to include social forces as causes of the people's health, and therefore reinvigorates epidemiology's historical role in targeting systems and structures for change.

An essential introduction to concepts of causation and causal inference that explores how our definitions of causes in epidemiology influence how we go about finding them and estimating their effects. A key goal of epidemiologic research is to uncover the causes of diseases and other health-related outcomes. But exactly what we mean by a "e;cause"e; and what types of phenomena qualify as causes, differ across historical periods, and is a contentious topic. The causal status of social phenomena has been, and continues to be, particularly precarious. In Causal Inference and the People's Health, Sharon Schwartz and Seth J. Prins offer both a synthesis of the dominant school of thought around causality and propose an approach that keeps causal concepts as an organizing principle without marginalizing social phenomena. The authors examine the implications of the "e;Causal Revolution,"e;--introduced in epidemiology as the Potential Outcomes framework--which initiated a paradigm shift across the social sciences. As the authors show, this shift influences the questions we ask, the methods we use, the narratives we construct about our study results, and thus the knowledge we use to fight for the people's health. The guiding principle of the Causal Revolution is simple but profound: researchers should specify if their goal is description, prediction, or causation. In other words, researchers should declare their causal goals even for observational studies. This principle not only produced important innovations, but it also reignited debates about the definition of causation, the causal status of important social constructs like class and gender, and the role of manipulation or intervention in causal inference. Interrogating these debates and embracing causal questions that identify and explain etiologic processes, Causal Inference and the People's Health offers a path forward that expands causal inference to include social forces as causes of the people's health, and therefore reinvigorates epidemiology's historical role in targeting systems and structures for change.