How to do a scoping review:draw the map, don't chase the answer

A systematic review asks a question that demands an answer: "Does this intervention work?" A scoping review asks about distribution rather than effect: "What research exists in this area, how much of it, and of what kind?"

The deliverables differ accordingly. The former produces a pooled conclusion; the latter produces a map—an organized picture of the study types, populations, methods, and time periods in a field, including the blank regions (the research gaps).

Scoping reviews use the PCC framework rather than PICO.

P (Population), C (Concept: what the research is about), and C (Context: the setting, location, or circumstances). The notable absence is Comparison—because this is not a review that compares effects. A question might read: "Research on telerehabilitation (Concept) for older adults (Population) in home settings (Context)."

Where a systematic review search narrows in on a specific question, a scoping review search is deliberately wide. It typically includes quantitative, qualitative, and practice-based study designs without restriction.

Don't fear catching too much; fear leaving holes in the map. That's the search philosophy here. Hit counts will be large, so plan your screening workload from the start.

Casting a wide net doesn't mean working without criteria. Inclusion and exclusion criteria derived from your PCC are set in advance, and screening is carried out with the same record-keeping discipline as a systematic review.

A dedicated reporting framework exists: the PRISMA extension for scoping reviews (PRISMA-ScR). For the details of conduct and reporting, follow PRISMA-ScR, the requirements of your target venue, and your supervisor's guidance.

Once studies are selected, the central activity is not deep critical appraisal but a process called charting. From each source, you extract author, year, country, population, study design, how the concept was operationalized, and main findings into a table, using fields you defined beforehand.

Note that risk-of-bias assessment is usually not required in a scoping review—the goal is to understand the distribution of research, not to grade its quality. Policies vary by purpose and venue, though, so confirm with your team.

With the chart complete, step back and look at the map. Which populations or settings attract the most research? Which designs dominate? How has the field shifted over time?

The most valuable discovery is the gap. "Only qualitative studies exist for this population." "Nothing has been published in this context for a decade." Naming the research gaps is the baton a scoping review passes to the next study—which may well be your own.

Because scoping reviews involve large volumes of references, they are actually well suited to AI assistance. Skimming abstracts, drafting chart fields, suggesting classification candidates—all of these save real time.

The principle, though, is the same as in any review: whatever goes into the chart gets verified against the original PDF, and any AI misreadings are corrected by hand. Interpretive fields like "how the concept was operationalized" can never be settled from a summary alone.

In Paperfy, each collected paper gets a single page holding its AI summary, figures, tables, and original PDF. As you preview papers for charting, you can add classification notes directly to the editable summary—"P: older adults / Concept: telerehabilitation / Context: home-based / Design: qualitative"—so your map material accumulates on the paper's own page.

When you're writing up and need to ask, "What was the basis for this classification?", the original text is right there on the same page. You get the breadth the map demands without losing the ability to return to your evidence.