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Evidence Reviews for Health & Social Care

What is data extraction?

Data extraction is the process of collecting relevant information from studies and organising it in a way that with help with data analysis and synthesis.

 

Data extraction can be challenging. It requires you to go back to your chosen articles and highlighting the relevant information that will answer your research question. Normally this involves extracting the data related to your chosen framework and its components e.g. PICO, PEO etc. To standardise and improve the validity of the results, it's crucial to create a data extraction form or table (Bettany-Saltikov, 2012, p. 96).

Considerations when creating a data extraction form/table

Top Tips for designing your data extraction table and column options
  • the study's author - first named author and year of publication
  • article title
  • journal title - full text? or abstract only?
  • study characteristics  - 
  • study design
  • where/when study conducted
  • inclusion/exclusion criteria
  • number of participants (including dropouts)
  • participants demographics e.g. age, sex, socio-economic status, ethnicity, co-morbidities etc.
  • interventions and comparators
  • study outcomes
  • analyses
  • for certain study designs (RCTs etc.) extract baseline participant data
  • additional notes

(Information adapted from Boland, 2017, p.97)

Here's some examples of data extraction forms that can be adapted for your own purpose:

 

If you're unsure, have a look at existing systematic reviews/literature reviews/scoping reviews on your topic to identify what information to collect. Look at their extraction tables/forms for ideas.

 Example of a data extraction table:

Note. Adapted From "8 Elements of person-centred care of older people in primary healthcare: a systematic literature review with thematic analysis" by Kegl et al. (2023).103–124. https://doi.org/10.1515/9783110786088-008 Copyright 2023 by De Gruyter Brill.

Table 3: Characteristics of chosen research papers

Author, Country

Research Design

Aim of Research

Sample Size

Main findings

Sarkisian et al. (2020)

USA

Qualitative study; focus group

Compares older adult & GP expectations of appointments

n=49 older adult      

n= 11 GPs

Reasons for appointments

Physical function, cognitive function, social function, pain

Older adult expressed that they felt like numbers not people, not involved in decision-making

GP stared at computer throughout conversation with no eye contact

Bastiaens et al. (2021)

Belgium

Qualitative study; interviews

Explores the views of older adult (aged 70 and over) on their involvement in primary healthcare in 11 European Countries

n=406 older adults (aged between 70 and 96)

Older adults want to be involved in their care and decision-making.

The study stressed the importance of good communication, interest in their problems, clear information, being reliable and supportive