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We'll be BACK on site, but NOT just yet!

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LCR is part of the overall University of Calgary re-entry strategy. We are working to restore some essential services as quickly as possible in a way that is safe for you - our users- and our staff and in the context of the overall campus re-entry plan. We will continue to offer our suite of online services and will also be expanding access to online books and resources. Virtual service hours today: 9:00 AM to 6:00 PM. 10:00 AM to 4:00 PM. Physical locations remain closed. You can return your books to the exterior book drop on the East side of the TFDL. More information on the Covid 19 page.

Systematic Reviews

Data Coding

Levels of Coding
  • Abstract screening
  • Study eligibility screening
  • Study content coding (full text coding)

Abstract Screening
  • Is the study relevant based on the title and abstract
  • Apply title/ abstract eligibility criteria
    • If yes, retrieve full text
    • If unclear, retrieve full text
    • Exclude obviously irrelevant studies
  • Include two trained raters working independently if possible

Study Content Coding (Full-Text Eligibility Screening)
  • Develop an eligibility screening form based on the inclusion / exclusion criteria
    • Complete for each study retrieved that is potentially eligible
    • Include reasons for ineligible studies (for PRISMA reporting)

Data extraction form:

  • Be consistent in the order and style you use to describe the information from each study
  • Provide instructions, decision rules, and training on the use of the data extraction form
  • Practice using the data extraction form
    • Compare completed pilot forms
    • Ensure that there is consistency in extracting data
    • Ideally two researchers should independently perform data extraction
  • Record any missing information as unclear or not described
    • Makes it clear that the information was not included in the study rather than you forgot to extract it

Coding System

Data extraction

A coding system to extract data (i.e. study details) from each study needs to be developed prior to collecting data (i.e. searching)

Standardized data extraction provides consistency, thereby potentially reducing bias, improving validity and reliability
  • Ensures that the same information is obtained (extracted) from each study
  • Identifies key components of the research including interventions, subjects, and methods
  • Should be developed early in the project
  • Pilot the data extraction form with a sample of studies
  • Keep track of revisions, corrections, amendments
  • If possible, pilot the exporting, analysis, and outputs of the data extraction form

 

Data Extraction Resources

Software for data extraction
Elamin, Mohamed B, et al. "Choice of data extraction tools for systematic reviews depends on resources and review complexity." Journal of clinical epidemiology 62.5 (2009):506-510. This article reviews different data extraction tools and offers advice on which tool to use to extract data.

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