76,545 research outputs found
Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation
Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas
Spatial interactions in agent-based modeling
Agent Based Modeling (ABM) has become a widespread approach to model complex
interactions. In this chapter after briefly summarizing some features of ABM
the different approaches in modeling spatial interactions are discussed.
It is stressed that agents can interact either indirectly through a shared
environment and/or directly with each other. In such an approach, higher-order
variables such as commodity prices, population dynamics or even institutions,
are not exogenously specified but instead are seen as the results of
interactions. It is highlighted in the chapter that the understanding of
patterns emerging from such spatial interaction between agents is a key problem
as much as their description through analytical or simulation means.
The chapter reviews different approaches for modeling agents' behavior,
taking into account either explicit spatial (lattice based) structures or
networks. Some emphasis is placed on recent ABM as applied to the description
of the dynamics of the geographical distribution of economic activities, - out
of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with
spatial structure, is used to illustrate the potential of such an approach for
spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book
"Complexity and Geographical Economics - Topics and Tools", P. Commendatore,
S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014
A Multi-Agent Simulation of Retail Management Practices
We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of
problems in a retail context. Specifically, we are working to understand the
relationship between human resource management practices and retail
productivity. Despite the fact we are working within a relatively novel and
complex domain, it is clear that intelligent agents do offer potential for
developing organizational capabilities in the future. Our multi-disciplinary
research team has worked with a UK department store to collect data and capture
perceptions about operations from actors within departments. Based on this case
study work, we have built a simulator that we present in this paper. We then
use the simulator to gather empirical evidence regarding two specific
management practices: empowerment and employee development
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