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Using Concept Mapping to Develop a Logic Model for the Prevention Research Centers Program

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  • English
    • Details Supporting Files You May Also Like

    Details:

    • Alternative Title:
      Prev Chronic Dis
    • Personal Author:
    • Description:
      Introduction

      Concept mapping is a structured conceptualization process that provides a visual representation of relationships among ideas. Concept mapping was used to develop a logic model for the Centers for Disease Control and Prevention's Prevention Research Centers Program, which has a large and diverse group of stakeholders throughout the United States. No published studies have used concept mapping to develop a logic model for a national program.

      Methods

      Two logic models were constructed using the data from the concept mapping process and program documents: one for the national level and one for the local level. Concept mapping involved three phases: 1) developing questions to generate ideas about the program's purpose and function, 2) gathering input from 145 national stakeholders and 135 local stakeholders and sorting ideas into themes, and 3) using multivariate statistical analyses to generate concept maps. Logic models were refined using feedback received from stakeholders at regional meetings and conferences and from a structured feedback tool.

      Results

      The national concept map consisted of 9 clusters with 88 statements; the local concept map consisted of 11 clusters with 75 statements. Clusters were categorized into three logic model components: inputs, activities, and outcomes. Based on feedback, two draft logic models were combined and finalized into one for the Prevention Research Centers Program.

      Conclusion

      Concept mapping provides a valuable data source, establishes a common view of a program, and identifies inputs, activities, and outcomes in a logic model. Our concept mapping process resulted in a logic model that is meaningful for stakeholders, incorporates input from the program's partners, and establishes important program expectations. Our methods may be beneficial for other programs that are developing logic models for evaluation planning.

    • Subjects:
    • Source:
      Prev Chronic Dis. 2006; 3(1).
    • Pubmed ID:
      16356359
    • Pubmed Central ID:
      PMC1500957
    • Document Type:
      Journal Article
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