The history of public health has focused on direct relationships between problems and solutions: vaccinations against diseases, ad campaigns targeting risky behaviors. But the accelerating pace and mounting intricacies of our lives are challenging the field to find new scientific methods for studying community health. The complexities of place (COP) approach is emerging as one such promising method. Place and Health as Complex Systems demonstrates how COP works, making an empirical case for its use in for designing and implementing interventions. This brief resource reviews the defining characteristics of places as dynamic and evolving social systems, rigorously testing them as well as the COP approach itself. The study, of twenty communities within one county in the Midwest, combines case-based methods and complexity science to determine whether COP improves upon traditional statistical methods of public health research. Its conclusions reveal strengths and limitations of the approach, immediate possibilities for its use, and challenges regarding future research. Included in the coverage: Characteristics of places and the complexities of place approach. The Definitional Test of Complex Systems. Case-based modeling using the SACS toolkit. Methods, maps, and measures used in the study. Places as nodes within larger networks. Places as power-based conflicted negotiations. Place and Health as Complex Systems brings COP into greater prominence in public health research, and is also valuable to researchers in related fields such as demography, health geography, community health, urban planning, and epidemiology.
By now, most academics have heard something about the new science of complexity. In a manner reminiscent of Einstein and the last hundred years of physics, complexity science has captured the public imagination. ® One can go to Amazon. com and purchase books on complexification (Casti 1994), emergence (Holland 1998), small worlds (Barabási 2003), the web of life (Capra 1996), fuzzy thinking (Kosko 1993), global c- plexity (Urry 2003) and the business of long-tails (Anderson 2006). Even television has incorporated the topics of complexity science. Crime shows ® ® such as 24 or CSI typically feature investigators using the latest advances in computational modeling to “simulate scenarios” or “data mine” all p- sible suspects—all of which is done before the crime takes place. The ® World Wide Web is another example. A simple search on Google. Com using the phrase “complexity science” gets close to a million hits! C- plexity science is ubiquitous. What most scholars do not realize, however, is the remarkable role sociologists are playing in this new science. C- sider the following examples. 0. 1 Sociologists in Complexity Science The first example comes from the new science of networks (Barabási 2003). By now, most readers are familiar with the phenomena known as six-degrees of separation—the idea that, because most large networks are comprised of a significant number of non-random weak-ties, the nodes (e. g. , people, companies, etc.
The history of public health has focused on direct relationships between problems and solutions: vaccinations against diseases, ad campaigns targeting risky behaviors. But the accelerating pace and mounting intricacies of our lives are challenging the field to find new scientific methods for studying community health. The complexities of place (COP) approach is emerging as one such promising method. Place and Health as Complex Systems demonstrates how COP works, making an empirical case for its use in for designing and implementing interventions. This brief resource reviews the defining characteristics of places as dynamic and evolving social systems, rigorously testing them as well as the COP approach itself. The study, of twenty communities within one county in the Midwest, combines case-based methods and complexity science to determine whether COP improves upon traditional statistical methods of public health research. Its conclusions reveal strengths and limitations of the approach, immediate possibilities for its use, and challenges regarding future research. Included in the coverage: Characteristics of places and the complexities of place approach. The Definitional Test of Complex Systems. Case-based modeling using the SACS toolkit. Methods, maps, and measures used in the study. Places as nodes within larger networks. Places as power-based conflicted negotiations. Place and Health as Complex Systems brings COP into greater prominence in public health research, and is also valuable to researchers in related fields such as demography, health geography, community health, urban planning, and epidemiology.
The author describes the experiences of a class of first-year medical students whom he followed as they faced three different exposures to death and dying. He also considers the factors that cause some students to view a cadaver as a formerly living human, while others see it as a learning tool.
By now, most academics have heard something about the new science of complexity. In a manner reminiscent of Einstein and the last hundred years of physics, complexity science has captured the public imagination. ® One can go to Amazon. com and purchase books on complexification (Casti 1994), emergence (Holland 1998), small worlds (Barabási 2003), the web of life (Capra 1996), fuzzy thinking (Kosko 1993), global c- plexity (Urry 2003) and the business of long-tails (Anderson 2006). Even television has incorporated the topics of complexity science. Crime shows ® ® such as 24 or CSI typically feature investigators using the latest advances in computational modeling to “simulate scenarios” or “data mine” all p- sible suspects—all of which is done before the crime takes place. The ® World Wide Web is another example. A simple search on Google. Com using the phrase “complexity science” gets close to a million hits! C- plexity science is ubiquitous. What most scholars do not realize, however, is the remarkable role sociologists are playing in this new science. C- sider the following examples. 0. 1 Sociologists in Complexity Science The first example comes from the new science of networks (Barabási 2003). By now, most readers are familiar with the phenomena known as six-degrees of separation—the idea that, because most large networks are comprised of a significant number of non-random weak-ties, the nodes (e. g. , people, companies, etc.
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