Learning from Evidence in a Complex World
Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts...
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Veröffentlicht in: | American journal of public health (1971) 2006-03, Vol.96 (3), p.505-514 |
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description | Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond. |
doi_str_mv | 10.2105/AJPH.2005.066043 |
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Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond.</description><identifier>ISSN: 0090-0036</identifier><identifier>EISSN: 1541-0048</identifier><identifier>DOI: 10.2105/AJPH.2005.066043</identifier><identifier>PMID: 16449579</identifier><identifier>CODEN: AJPEAG</identifier><language>eng</language><publisher>Washington, DC: Am Public Health Assoc</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; Behavior ; Biological & chemical terrorism ; Biological and medical sciences ; Cardiovascular disease ; Citrus fruits ; Communication ; Computer Simulation ; Cystic fibrosis ; Drug use ; Experiments ; Failure ; Feedback ; GDP ; Gross Domestic Product ; Health Policy ; Health services ; Humans ; Infant mortality ; Learning ; Life expectancy ; Management ; Medical errors ; Medical sciences ; Miscellaneous ; Mortality ; Pathogens ; Public health ; Public Health Matters ; Public health. 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Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. 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subjects | Acquired immune deficiency syndrome AIDS Behavior Biological & chemical terrorism Biological and medical sciences Cardiovascular disease Citrus fruits Communication Computer Simulation Cystic fibrosis Drug use Experiments Failure Feedback GDP Gross Domestic Product Health Policy Health services Humans Infant mortality Learning Life expectancy Management Medical errors Medical sciences Miscellaneous Mortality Pathogens Public health Public Health Matters Public health. Hygiene Public health. Hygiene-occupational medicine Research - organization & administration Scientific method Studies Systems Theory Tropical diseases United States |
title | Learning from Evidence in a Complex World |
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