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Increasing value and reducing waste in data extraction for systematic reviews: tracking data in data extraction forms

Overview of attention for article published in Systematic Reviews, August 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
86 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
38 Mendeley
citeulike
1 CiteULike
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Title
Increasing value and reducing waste in data extraction for systematic reviews: tracking data in data extraction forms
Published in
Systematic Reviews, August 2017
DOI 10.1186/s13643-017-0546-z
Pubmed ID
Authors

Farhad Shokraneh, Clive E. Adams

Abstract

Data extraction is one of the most time-consuming tasks in performing a systematic review. Extraction is often onto some sort of form. Sharing completed forms can be used to check quality and accuracy of extraction or for re-cycling data to other researchers for updating. However, validating each piece of extracted data is time-consuming and linking to source problematic.In this methodology paper, we summarize three methods for reporting the location of data in original full-text reports, comparing their advantages and disadvantages.

Twitter Demographics

The data shown below were collected from the profiles of 86 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 13%
Student > Ph. D. Student 5 13%
Student > Bachelor 4 11%
Student > Master 4 11%
Librarian 4 11%
Other 8 21%
Unknown 8 21%
Readers by discipline Count As %
Medicine and Dentistry 10 26%
Computer Science 4 11%
Nursing and Health Professions 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Sports and Recreations 2 5%
Other 9 24%
Unknown 8 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 49. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 February 2020.
All research outputs
#676,604
of 21,653,050 outputs
Outputs from Systematic Reviews
#86
of 1,876 outputs
Outputs of similar age
#15,877
of 289,724 outputs
Outputs of similar age from Systematic Reviews
#1
of 13 outputs
Altmetric has tracked 21,653,050 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,876 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 289,724 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.