Outcome Mapping
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Outcome Mapping Practitioner Guide
Intentional Design
Given the potential for uncertainties to arise in any situation, the realistic and responsible approach for an intervention would be to treat most situations as complex. Assuming complexity and acting accordingly has implications for design and planning. Complex or uncertain situations call for engagement, learning and adaptation as factors emerge and understanding changes. The Intentional Design can be used in many kinds of interventions, however it is most useful wher... Read more ▼
Given the potential for uncertainties to arise in any situation, the realistic and responsible approach for an intervention would be to treat most situations as complex. Assuming complexity and acting accordingly has implications for design and planning. Complex or uncertain situations call for engagement, learning and adaptation as factors emerge and understanding changes. The Intentional Design can be used in many kinds of interventions, however it is most useful where influential factors and actors and results are expected to emerge. The Intentional Design lends itself to planning a framework which is sensitive to both expected and unanticipated events, actions and relationships.
Examples of OM Intentional Design frameworks
Here you can find examples of intentional design frameworks developed by different programmes / organisations
Nuggets related to this section:
- Intentional Design of the Teacher Education and Child Vulnerability Programme of VVOB Zimbabwe (2008-2013) by Jan Van Ongevalle
- Intentional Design of the Climate Change, Agriculture and Food Security (CCFAS) programme in Burkina Faso, Niger, Ghana and Senegal. by Dr Jacques Somda
- Working with nested OM frameworks by Jan Van Ongevalle
- Simplifying heavy intentional designs (reducing numbers of progress markers) by Jan Van Ongevalle
Different interventions, different designs
Given the potential for uncertainties to arise in any situation, the realistic and responsible approach for an intervention would be to treat most situations as complex. Assuming complexity and acting accordingly has implications for design and planning. Complex or uncertain situations call for engagement, learning and adaptation as factors emerge and understanding changes. In many cases, the challenge is to iteratively, throuhg engagement, improve the performance of the intervention. This is not the case when with testing a specific intervention 'model' or defined compnent like a training module, curriculum, drug, vaccine or software program. Such interventions are designed to methodologically isolate the effects of the 'model' as defined at the outset. Thus by establishing its costs, efectiveness and limitations the defined intervention component can be assessed for further or expanded use. Adjustments can be made once the testing is concluded and the effects assessed - not during the implementation process. The concern here is often called 'fidelity to the model', that is keeping the intervention components constant, while controlling and accounting for contextual and other influential factors.
Seeking social, ecological or economic 'sustainability' or 'resilience' (adaptive viability in the face of unknown futures) is generally a complex intervention. There are no testable or transferable models for influencing stakeholder competence, ownership, commitment and action. Each context will require probing, sensing and responding. Almost by defintion, interventions aimed at resilience and sustainability in their outcomes will entail exploring unknown territory; experimenting with new or adjusted strategies; and responding to the emerging patterns of interaction among individuals and organizations in their dynamic contexts. Rather than trying to rule out influenctial external or contextual factors, complexity-oriented interventions rule them in, striving to understand and engage with them. Given the range of unknown possibilities, assuming complexity and acting accordingly makes a lot of sense.
Clearly the planning for these two kinds of interventions is different. At the model testing end of the spectrum, the intervention component is thoroughly detailed and planning is focused on consistently applying it under known, standarized circumstances. At the exploratory end of the specturm, planning focuses on using feedback to adapt the intervention ot the circumstances encountered. In the former, planning is focused on creating certainty around an application. In the latter planning includes seeking and responding to uncertainty by getting and using feedback on how things are going. The greater the complexity in a sitation the greater the importance of monitoring to support learning and adaptation.
Outcome Mapping can be used in both kinds of interventions, however it is most usefufl where influential factors and actors and results are expected to emerge. OM can be used to build a monitoring framework which is sensitive to both expected and unexpected events.
Nuggets related to this section:
- Developing a theory of change with Outcome Mapping by Simon Hearn