Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram.
Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram.
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DOI：
10.1371/journal.pone.0165459
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Allender S
中科院分区：
文献类型：
--
作者：
McGlashan J;Johnstone M;Creighton D;de la Haye K;Allender S
小组开发的因果环图捕捉了对复杂问题的共同理解，并提供了指导干预的可视化工具。本文探索了网络分析方法的应用，作为一种新的方法，可以定量地了解肥胖原因环图的结构，为干预设计提供信息。识别因果环图的结构特征可能会为复杂系统的新特性提供新的见解，而分析中心驱动因素有可能确定杠杆点。结果发现，肥胖因果环图的结构类似于通常观察到的以有效传播信息而闻名的经验网络。已知的肥胖驱动因素被发现是社区肥胖预防的最核心变量，以及其他独特的变量。虽然因果环图通常特定于单个社区，但分析方法提供了对比和比较复杂问题的多个因果环图的方法。
Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.
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影响因子：
1
作者：
Brandes, U
通讯作者：
Brandes, U
DOI：
10.1146/annurev-clinpsy-050212-185608
发表时间：
2013-01-01
期刊：
ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 9
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
158.5
作者：
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通讯作者：
Fowler, James H.
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
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通讯作者：
Lefebvre, Etienne