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Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram
Jaimie McGlashan1,2, Michael Johnstone2, Doug Creighton2
1Global Obesity Centre, Deakin University, Geelong, Australia.
Plos One
|October 28, 2016
Summary
Network analysis of obesity causal loop diagrams reveals key drivers and efficient information spread patterns. This approach offers quantitative insights for designing targeted public health interventions and comparing complex system dynamics.
Area of Science:
- Systems Science
- Public Health
- Network Analysis
Background:
- Causal loop diagrams (CLDs) visually represent complex problems and guide interventions.
- Quantitative analysis of CLD structure can reveal emergent properties and identify intervention leverage points.
Purpose of the Study:
- To apply network analytic methods to an obesity causal loop diagram.
- To gain quantitative insights into the structure of the obesity CLD for intervention design.
Main Methods:
- Network analysis was applied to an obesity causal loop diagram.
- Centrality measures were used to identify key variables and drivers.
Main Results:
- The obesity CLD structure resembles empirical networks known for efficient information spread.
- Known obesity drivers were identified as central variables, alongside unique community-specific factors.
Conclusions:
- Network analysis provides a quantitative approach to understanding CLD structure and complex systems.
- This method can identify critical drivers for obesity intervention and facilitate comparison across different CLDs.
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