OLM Assumptions: Problems and Priorities


How you structure the problem is critical for how and where you begin to address the needs or service gaps that you identify. Keeping with the example of addressing child hunger, we must articulate the problem to build some scaffolding for systematic intervention. If you say that children are hungry because their parents don’t have financial knowledge, your intervention will likely focus on financial literacy for parents. If you say that kids are hungry because they don’t have nutritious meals available due to a food desert, you will likely intervene with a food pantry program offering fresh vegetables. It could also be the case that children choose less nutritious foods even when presented with health options. With this as the articulated problem, you would seek to influence their health choice behavior.

How you structure the problem determines how you approach and build a program of intervention. In this way, the assumptions inform the other columns in the outcome logic model. Even though they aren’t tied directly to the inputs or activities, they are connected.


Prioritizing the needs and interventions is a must because the problems you identify will be multifaceted and complex. If you find that the financial literacy, food desert, and healthy food choices all play a part in the nutrition of the children, which will you tackle first? Or, more precisely, how will you structure the program to address each of the barriers to health and wellbeing?

Priority must consider the time-to-deployment as well as the most critical item and the easiest win. In addition to asking which is first, you must determine an acceptable balance between the time-to-deployment and the needs as they present. If you have children who are going without any food, your priorities will be different. Addressing the food desert long-term may take 6 months to get up and running. A food pantry stocking donated non-perishables may be an interim solution.  Your priority determination must factor in this complexity. You don’t want to put your target at a disadvantage while you scale the intervention.