Action Logic Modelling

2. Action Logic Modelling • Logic Models communicate a vision for an intervention as a solution to a public health nutrition (PHN) problem to: • funding agencies, management, key stakeholders, target population • Logic Models conceptualise an intervention into a diagram that clearly illustrates the strategy mix, assumptions and causal chain of change expected results, that will contribute to the achievement of goals and objectives • ‘A picture paints a thousand words’ Logic Modelling

3. Logic Modelling

4. What is a Logic Model? • A logic model shows the assumptions underlying the intervention activities, and graphically depicts the association between the main intervention strategies, and the goals, objectives, target group, indicators and resources • A well constructed logic model is like road map that explains where you are going, how you will get there and will identify when you arrive • A logic model is a communication tool for all stakeholders to understand: • the overall structure and function of an intervention • how intervention strategies link with goals and objectives • Common assumptions, intensions and actions Logic Modelling

5. What is a Logic Model? • Logic models are useful in planning and evaluating PHN interventions: • Planning • Identify gaps and inconsistencies within an intervention • Involving stakeholders to participate in intervention planning Evaluation • Matching intervention strategies with objectives and indicators of success • Helps assess when an intervention is ready to be evaluated (evaluability assessment) • Assist identify the success indicators that are critical for intervention evaluation Logic Modelling

6. Types of Logic Models • Logic models come in various shapes and forms, depending on the nature of the intervention the needs and preferences of the stakeholders • Logic models are usually depicted in chart form with: • lines or arrows show relationships between intervention features - strategies, objectives, target population, partnerships etc • intervention features are usually presented in boxes or ovals • Boxes can be vertical, horizontal, circular or more complex and dynamic Logic Modelling

7. Example logic model layouts Taylor-Powell and Henert (2008) Logic Modelling

8. Logic Model Elements • Logic models contain several key elements that are necessary to explain the link between the problem, intervention and the impact • Logic model elements: • Situation – explains the relevance of the intervention – what the problem is and who is affected - intervention goal and target group • Inputs – the resources and capacity put into the intervention • Outputs – the activities and strategies of the intervention • Outcomes – the results or intended impact of the intervention – objectives and sub-objectives • External Influences – the social, political, physical and institutional environments that can influence the outcomes Logic Modelling

9. Logic Modelling in PHN Practice • Logic modelling in PHN intervention management draws upon the previous determinant analysis and assists develop evaluation indicators • Consistent with the capacity building principles that underpin PHN practice, logic model development should involve extensive consultation with stakeholders. • Involving stakeholders: • tests the feasibility of the proposed intervention • creates increased agreement with the intervention design • increases participation in intervention implementation Logic Modelling

10. Developing a Logic Model • The recommended process for developing a logic model is to work through four key tasks: • Preparation – community & stakeholder engagement and consultation, capacity analysis, timeline, decision making • Intelligence – gathering information re the PHN problem, its determinants and strategy options • Creation – decide how much information will be included and the type of logic model • Review – presenting and discussing the logic model with key stakeholders • The first two tasks in this process have largely been completed in the previous steps (1-9) of the PHN intervention management bi-cycle Logic Modelling

11. Logic Modelling