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The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.
The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.
The identified lives effect describes the fact that people demonstrate a stronger inclination to assist persons and groups identified as at high risk of great harm than those who will or already suffer similar harm, but endure unidentified. As a result of this effect, we allocate resources reactively rather than proactively, prioritizing treatment over prevention. For example, during the August 2010 gold mine cave-in in Chile, where ten to twenty million dollars was spent by the Chilean government to rescue the 33 miners trapped underground. Rather than address the many, more cost effective mine safety measures that should have been implemented, the Chilean government and international donors concentrated efforts in large-scale missions that concerned only the specific group. Such bias as illustrated through this incident raises practical and ethical questions that extend to almost every aspect of human life and politics. What can social and cognitive sciences teach us about the origin and triggers of the effect? Philosophically and ethically, is the effect a "bias" to be eliminated or is it morally justified? What implications does the effect have for health care, law, the environment and other practice domains? This volume is the first to take an interdisciplinary approach toward answering this issue of identified versus statistical lives by considering a variety of perspectives from psychology, public health, law, ethics, and public policy.
How the most important statistical method used in many of the sciences doesn't pass the test for basic common sense
"Clinical versus Statistical Prediction" is Paul Meehl's famous examination of benefits and disutilities related to the different ways of combining information to make predictions. It is a clarifying analysis as relevant today as when it first appeared. A major methodological problem for clinical psychology concerns the relation between clinical and actuarial methods of arriving at diagnoses and predicting behavior. Without prejudging the question as to whether these methods are fundamentally different, we can at least set forth the obvious distinctions between them in practical applications. The problem is to predict how a person is going to behave: What is the most accurate way to go about this task? "Clinical versus Statistical Prediction" offers a penetrating and thorough look at the pros and cons of human judgment versus actuarial integration of information as applied to the prediction problem. Widely considered the leading text on the subject, Paul Meehl's landmark analysis is reprinted here in its entirety, including his updated preface written forty-two years after the first publication of the book. This classic work is a must-have for students and practitioners interested in better understanding human behavior, for anyone wanting to make the most accurate decisions from all sorts of data, and for those interested in the ethics and intricacies of prediction. As Meehl puts it, " "When one is dealing with human lives and life opportunities, it is immoral to adopt a mode of decision-making which has been demonstrated repeatedly to be either inferior in success rate or, when equal, costlier to the client or the taxpayer.""
"Now with a new afterword by the author"--Back cover.
Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies.
The issue of medical ethics, from thorny moral questions such as euthanasia and the morality of killing to political questions such as the fair distribution of health care resources, is rarely out of today's media. This area of ethics covers a wide range of issues, from mental health to reproductive medicine, as well as including management issues such as resource allocation, and has proven to hold enduring interest for the general public as well as the medical practitioner. This Very Short Introduction provides an invaluable tool with which to think about the ethical values that lie at the heart of medicine. This new edition explores the ethical reasoning we can use to approach medical ethics, introducing the most important 'tools' of ethical reasoning, and discussing how argument, thought experiments, and intuition can be combined in the consideration of medical ethics. Considering its practical application, Tony Hope and Michael Dunn explore how medical ethics supports health professionals through the growing use of ethics expertise in clinical settings. They also contemplate the increasingly important place of medical ethics in the wider social context, particularly in this age of globalization, not only in healthcare practice, but also policy, discussions in the media, pressure group and activism settings, and in legal judgments. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
In the United States, some populations suffer from far greater disparities in health than others. Those disparities are caused not only by fundamental differences in health status across segments of the population, but also because of inequities in factors that impact health status, so-called determinants of health. Only part of an individual's health status depends on his or her behavior and choice; community-wide problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, interpersonal violence, and decaying neighborhoods also contribute to health inequities, as well as the historic and ongoing interplay of structures, policies, and norms that shape lives. When these factors are not optimal in a community, it does not mean they are intractable: such inequities can be mitigated by social policies that can shape health in powerful ways. Communities in Action: Pathways to Health Equity seeks to delineate the causes of and the solutions to health inequities in the United States. This report focuses on what communities can do to promote health equity, what actions are needed by the many and varied stakeholders that are part of communities or support them, as well as the root causes and structural barriers that need to be overcome.