Your codebook is ready. You’ve meticulously defined the EPIS phases, inner and outer context factors, and the specific policy variables from Crable et al. Now, the real work begins: pulling the data out of individual studies and weaving it into a coherent story. This process, called data extraction and synthesis, moves you from a list of coded articles to a meaningful analysis of the evidence.

The goal isn't just to count how many times a code appears. It’s to understand the relationships between policy, context, and the different phases of implementation across a whole body of literature. We need a systematic way to organize this information before we can identify patterns.

The first step is creating a data extraction table. Think of this as a structured spreadsheet where each row represents a single key finding from an article, and the columns break down that finding according to your EPIS codebook. This organized approach ensures you capture the same type of information from every study, whether it's a qualitative case study or a large quantitative analysis.

This table does more than just log data. It forces you to connect specific findings to the EPIS framework. For qualitative studies, you might pull a direct quote. For quantitative ones, you'd summarize a statistical result. The key is to maintain the link between the evidence and its context within the implementation journey.

Once your table is populated, you can start synthesizing. This is where you look for patterns across the rows. Are certain policy variables consistently linked to challenges in the Implementation phase? Do specific inner context factors, like strong leadership, appear frequently as facilitators during the Sustainment phase?

Aggregating your findings is crucial. You can begin by simply counting how often factors appear in each phase. For example, you might find that 'Financing' (a policy variable) was cited in 80% of studies as a key barrier during the Preparation phase, but 'Regulatory Mandates' were more influential during Implementation. This kind of cross-study comparison is the core of a scoping review.

Visualizing these patterns can be incredibly powerful. You don’t need complex statistical software. Simple charts or diagrams can illustrate the connections you’ve uncovered. A flow diagram can show how policy factors influence each other across the EPIS phases, revealing bottlenecks or critical transition points.

By the end of this process, you should have a clear, evidence-backed narrative. You can state not only what factors matter for implementation, but also when they matter (which EPIS phase) and how they are shaped by high-level policy. This synthesis is the ultimate contribution of your scoping review.

This method transforms a collection of individual studies into a powerful, synthesized overview that can inform future policy and practice.