Screening decides which studies make it into your nest. Tagging decides what you actually learn from them. This is the module where a set of included records becomes structured, comparable data — by building a Tag Hierarchy that mirrors the concepts your review is about, then applying those tags to each study.
It’s worth thinking of the hierarchy as the data-extraction framework for your review, custom-built for your question. Everything you can analyse later in Qualitative Synthesis is determined by what you capture here, which is why the design of the hierarchy matters more than any single tagging action.
Tagging has three parts: you build a hierarchy, you apply its tags to studies (by hand or with AI), and the result feeds your synthesis. The sections below orient you to each and the Configuring and Editing the Tag Hierarchy page covers the mechanics in full.
What a Tag Hierarchy is #
A Tag Hierarchy is your concepts of interest, organised from broad to specific: Root tags (your most overarching concepts) break down into parent and child tags for detail. “Study Design,” for example, is a parent tag whose children might be “Randomized Controlled Trial” and “Prospective Cohort Study.”
The shape you build is the shape your synthesis takes, so the hierarchy should reflect the information you intend to extract and report. There’s no limit on how many tags or layers you can create, but the most useful guiding principle to note: every tag becomes a question asked of every study, so build only what your synthesis genuinely needs.
Form-based vs Standard tagging #
Tags can be presented to reviewers in two ways, and the choice affects how much structure your extraction has.
Form-based (the default) presents your tags as a structured set of questions and answers — a questionnaire completed for each study. This keeps extraction consistent across reviewers and studies, and it’s what unlocks richer data capture (tables, numeric values, pre-set options).
Standard mode is lighter: tags appear as a dropdown of concepts to apply as relevant, with no question-and-answer structure but you would typically still organize the hierarchy in the same way as in Form-based. It suits reviews where you simply need to mark which concepts are present. Switch in settings.
Switching between modes is non-destructive and no data is lost either way.
Three ways to build your hierarchy #
You don’t have to start from a blank canvas. There are three routes, and all of them produce a hierarchy you can edit afterward:
- From scratch gives you full control — you configure each question and its answers yourself.
- Import a template lets you start from a vetted structure, either one of Nested Knowledge’s public templates or one belonging to your organization, then adapt it.
- Let AI draft it — Core Smart Tags builds a PICO-style hierarchy from your research question, and can apply the tags from abstracts for you for easy filtering. Smart Config: Tagging builds a more detailed hierarchy for deeper extraction, which can be edited before tags are applied.
Full instructions for all three are on the Configuring and Editing the Tag Hierarchy page.
Applying tags to studies #
Once your hierarchy is configured, tags can be applied to records during Screening, in the Tagging module, or at any time in Study Inspector. You can also run Adaptive Smart Tags to recommend or directly apply tags from each study’s abstract or full text.
How Tagging connects to the rest of your review #
Tagging’s primary outputs are Qualitative Synthesis and Dashboards, where living tables and insights are generated, updated and exported, all built directly on your hierarchy’s structure.
One thing worth knowing up front: the same hierarchy is also the basis for configuring Data Elements and Interventions in Meta-Analytical Extraction. If you plan to run an in-nest meta-analysis as well, a little forethought about how you structure your tags pays off in both places.
Next: Configure your Tag Hierarchy, then apply tags to your studies.
