If you want to move a successful intervention from here (where it was first identified) to there (a plurality of new settings), spend your time understanding the context of the intervention. Understand the context of success. Implementation Science—the science of moving successful interventions from here to there—assumes a real (in the world effect) that can be generalised to new settings. In our latest (open access) article, recently published in Social Science and Medicine, we re-imagine that presumption.
As researchers and development specialists, we are taught to focus on causes as singular things: A causes B. Intervention A reduces infant mortality (B1), increases crop yields (B2), keeps girls in school longer (B3), or…. When we discover the new intervention that will improve the lives of the many, we naturally get excited. We want to implement it everywhere. And yet, the new intervention so often fails in new settings. It isn’t as effective as advertised and/or it’s more expensive. The intervention simply does not scale-up and potentially results in harm. Effort and resources are diverted from those things that already work better there to implement the new intervention, which showed so much promise in the original setting, here.
The intervention does not fail in new settings because the cause-effect never existed. It fails in new settings because causes are local. The effect that was observed here was not caused by A alone. The intervention was not a singular cause. A causes B within a context that allows the relationship between cause and effect to be manifest. The original research in which A was identified had social, economic, cultural, political, environmental, and physical properties. Some of those properties are required for the realisation of the cause-effect. This means that generalisation is really about re-engineeering context. We need to make sure the target settings have the the right contextual factors in place for the intervention to work. We are re-creating local contexts. The implementation problem is one of understanding the re-engineering that is required.