Changes

==Appendix B: Developing a logic model for regulatory activities==

==Appendix B: Developing a logic model for regulatory activities==

A logic model (also known as results logic and theory of action or intervention) is a graphic (usually accompanied by text) that tells the story of a regulatory initiative. It connects the inputs (resources) and activities (internal processes) to the outputs (products or services generated from the activities), the groups reached (partners, intermediaries, and target groups), and the expected outcomes of that initiative (the sequence of changes in groups outside the control of the regulator).

A logic model (also known as results logic and theory of action or intervention) is a graphic (usually accompanied by text) that tells the story of a regulatory initiative. It connects the inputs (resources) and activities (internal processes) to the outputs (products or services generated from the activities), the groups reached (partners, intermediaries, and target groups), and the expected outcomes of that initiative (the sequence of changes in groups outside the control of the regulator).

[[File:Pmep-pmre3-eng.gif|thumb|500x500px|Figure 2: Example of How Problems and Needs Inform the Results Logic of an Environmental Initiative]]

[[File:Pmep-pmre3-eng.gif|thumb|400x400px|Figure 2: Example of How Problems and Needs Inform the Results Logic of an Environmental Initiative|alt=]]

As illustrated in the following example, a logic model is commonly depicted as a graphic.

As illustrated in the following example, a logic model is commonly depicted as a graphic.

[[File:Pmep-pmre2-eng.gif|alt=To depict a regulatory performance story, it is useful to start with a structured understanding of the problem, need, risk, or harm before developing the results logic. The advantages of defining the problems, needs, risks, or harms before defining results include the following:    It supports the first step in the regulatory process as described in the Cabinet Directive on Streamlining Regulation: "identify the problem or policy issue." If we have not properly identified the problem, then we have likely not identified an appropriate solution.  Identifying problems, risks, or harms serves to set the vital context. In fact, they can directly set the terms for and define the inputs, activities, outputs, and outcomes stated in the logic model. Figure 2 shows how this works.|left|thumb|500x500px|Figure 1: Logic Model Example]]

[[File:Pmep-pmre2-eng.gif|alt=To depict a regulatory performance story, it is useful to start with a structured understanding of the problem, need, risk, or harm before developing the results logic. The advantages of defining the problems, needs, risks, or harms before defining results include the following:    It supports the first step in the regulatory process as described in the Cabinet Directive on Streamlining Regulation: "identify the problem or policy issue." If we have not properly identified the problem, then we have likely not identified an appropriate solution.  Identifying problems, risks, or harms serves to set the vital context. In fact, they can directly set the terms for and define the inputs, activities, outputs, and outcomes stated in the logic model. Figure 2 shows how this works.|left|thumb|500x500px|Figure 1: Logic Model Example]]