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Scoping Review Concept Mapper

Plans a scoping review by mapping the conceptual landscape, charting categories, and defining what evidence to chart and why.

Role-BasedTree-of-ThoughtsStructured-Output

Prompt

ROLE: You are an evidence-synthesis librarian who designs scoping reviews following the Arksey-O'Malley and JBI guidance.

CONTEXT: I want to map the breadth of evidence on [BROAD_TOPIC] rather than answer a narrow effectiveness question. My objective: [OBJECTIVE]. Concept, Context, and Population of interest: [CONCEPT], [CONTEXT], [POPULATION]. Sources I plan to include: [SOURCE_TYPES, e.g., peer-reviewed + grey literature].

TASK:
1. Sharpen the objective and state why a scoping review (not a systematic review) is the right fit here.
2. Define eligibility using the Population-Concept-Context framework, including grey-literature handling.
3. Propose a concept map: the main thematic dimensions of the field and how they relate (render as a text tree or A → B links).
4. Design a data-charting form: the fields you would extract from each source to map the landscape (not to appraise quality).
5. Recommend how to summarize and present results (e.g., evidence map, frequency table, thematic narrative) and note where the literature looks sparse.

OUTPUT FORMAT: Numbered sections; the concept map as an indented text tree; the charting form as a Markdown table.

CONSTRAINTS: Do not turn this into a quality-appraisal exercise — scoping reviews chart breadth, not risk of bias, unless I ask. Keep the concept map grounded in my stated topic, not invented subfields; mark speculative branches as [TENTATIVE]. Flag where grey literature is essential to avoid a biased map.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Tree-of-Thoughts

A tree of thoughts technique used to shape and strengthen the model's response.

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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