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Scoping Review

There are several steps involved in the completion of a Scoping Review. The following guide to the Scoping Review process provides a summary of the key steps that are involved.

Extract Data

At this stage of your Scoping Review, you should set about the task of systematically extracting key information to enable comparison and mapping.

There are several free and subscription-based resources that can assist during the Scoping Review process. Many of these tools are designed to assist with the key stages of the process, including title and abstract screening, data synthesis, and critical appraisal. Some are designed to assist the review team throughout the entire process, including protocol development, reporting findings etc.

  • Rayyan: Rayyan is a web-tool designed to help researchers working on scoping and systematic reviews, as well as other knowledge synthesis projects, by dramatically speeding up the process of screening and selecting studies. Note: Rayyan offers a subscription-based service and a free version for early career researchers.
  • Covidence: Covidence is an online software tool designed to streamline the process of conducting a Scoping Review or more detailed Systematic Review. You can use Covidence to collaborate with a team of reviewers to screen results (at both title/abstract and full text stages), complete data extraction and work on risk of bias.
  • DistillerSR: DistillerSR automates the management of literature collection, screening, and assessment using AI and intelligent workflows. From a scoping review to a rapid review or Systematic review, DistillerSR simplifies the Review process and helps the review team produce transparent, audit-ready, and compliant results.
  • Excel or Google Sheets can also be used during the article screening process and offer simple, customizable tables for data extraction.

 

Suggested data fields to use when extracting data

Field Description/Example
Study ID Author(s) and publication year
Study Design Type of study (qualitative, quantitative, review, etc)
Population Characteristics such as age, student type
Concept / Intervention What was studied or the focus of the study
Context Setting / location or any relevant environmental factors
Outcomes / Key Findings Main results related to your research question
Notes Any additional remarks or relevant details

Extracted Data using the above Suggested Data Field Example

Study ID (Author, Year) Study Design Population / Participants Concept/ Intervention Context (Setting) Key Findings / Outcomes Notes / Comments
Smith et al., 2020 Cross-sectional 500 university students Online Learning US University Increased anxiety linked to online exams Survey-based, self-reported
Lee & Kim, 2019 Qualitative 30 college undergraduates Virtual learning experience South Korean University Mixed feelings: convenience vs isolation Interviews conducted

Tips:

  • Use dropdown lists in “Study Design” (e.g., Qualitative, Quantitative, Mixed Methods, Review etc).
  • In Excel or Google Sheets, you can freeze the header row for easy navigation.
  • Using Excel or Google Sheets, you can add filters for each column.