Advances In Data Analysis For Prevention Intervention Research
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Reducing Risks for Mental Disorders
Author | : Institute of Medicine |
Publisher | : National Academies Press |
Total Pages | : 636 |
Release | : 1994-01-01 |
Genre | : Medical |
ISBN | : 0309049393 |
The understanding of how to reduce risk factors for mental disorders has expanded remarkably as a result of recent scientific advances. This study, mandated by Congress, reviews those advances in the context of current research and provides a targeted definition of prevention and a conceptual framework that emphasizes risk reduction. Highlighting opportunities for and barriers to interventions, the book draws on successful models for the prevention of cardiovascular disease, injuries, and smoking. In addition, it reviews the risk factors associated with Alzheimer's disease, schizophrenia, alcohol abuse and dependence, depressive disorders, and conduct disorders and evaluates current illustrative prevention programs. The models and examination provide a framework for the design, application, and evaluation of interventions intended to prevent mental disorders and the transfer of knowledge about prevention from research to clinical practice. The book presents a focused research agenda, with recommendations on how to develop effective intervention programs, create a cadre of prevention researchers, and improve coordination among federal agencies.
Scientific Methods for Prevention Intervention Research
Author | : Arturo Cázares |
Publisher | : |
Total Pages | : 294 |
Release | : 1994 |
Genre | : Substance abuse |
ISBN | : |
Handbook of Diversity Issues in Health Psychology
Author | : Pamela M. Kato |
Publisher | : Springer Science & Business Media |
Total Pages | : 453 |
Release | : 2007-07-27 |
Genre | : Psychology |
ISBN | : 0585275726 |
The field of health psychology has grown dramatically in the last decade, with exciting new developments in the study of how psychological and psychosocial processes contribute to risk for and disease sequelae for a variety of medical problems. In addition, the quality and effectiveness of many of our treatments, and health promotion and disease prevention efforts, have been significantly enhanced by the contributions of health psychologists (Taylor, 1995). Unfortunately, however, much of the theo rizing in health psychology and the empirical research that derives from it continue to reflect the mainstream bias of psychology and medicine, both of which have a primary focus on white, heterosexual, middle-class American men. This bias pervades our thinking despite the demographic heterogeneity of American society (U. S. Bureau of the Census, 1992) and the substantial body of epidemiologic evidence that indicates significant group differences in health status, burden of morbidity and mortality, life expectancy, quality of life, and the risk and protective factors that con tribute to these differences in health outcomes (National Center for Health Statistics, 1994; Myers, Kagawa-Singer, Kumanyika, Lex, & M- kides, 1995). There is also substantial evidence that many of the health promotion and disease prevention efforts that have proven effective with more affluent, educated whites, on whom they were developed, may not yield comparable results when used with populations that differ by eth nicity, social class, gender, or sexual orientation (Cochran & Mays, 1991; Castro, Coe, Gutierres, & Saenz, this volume; Chesney & Nealey, this volume).
Problems of Drug Dependence
Author | : College on Problems of Drug Dependence (U.S.). Scientific Meeting |
Publisher | : |
Total Pages | : 552 |
Release | : 1996 |
Genre | : Drug abuse |
ISBN | : |
Statistical Strategies for Small Sample Research
Author | : Rick H. Hoyle |
Publisher | : SAGE |
Total Pages | : 394 |
Release | : 1999-03-30 |
Genre | : Mathematics |
ISBN | : 9780761908869 |
This book provides encouragement and strategies for researchers who routinely address research questions using data from small samples. Chapters cover such topics as: using multiple imputation software with small sets; computing and combining effect sizes; bootstrap hypothesis testing; application of latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.