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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Response to ‘Increasing value and reducing waste in data extraction for systematic reviews: tracking data in data extraction forms’
|
|---|---|
| Published in |
Systematic Reviews, January 2018
|
| DOI | 10.1186/s13643-018-0677-x |
| Pubmed ID | |
| Authors |
Jens Jap, Ian J. Saldanha, Bryant T. Smith, Joseph Lau, Tianjing Li |
| Abstract |
ᅟ: This is a response to a Letter. Data abstraction is a time-consuming and error-prone systematic review task. Shokraneh and Adams categorize available techniques for tracking data during data abstraction into three methods: simple annotation, descriptive addressing, and Cartesian coordinate system. While we agree with the categorization of the techniques, we disagree with the authors' statement that descriptive addressing is a PDF-independent method, i.e., any sort of descriptive addressing must reference a specific version of PDF file and not just any PDF of said report. Different versions of PDFs of the same report might place text and tables on different locations of the same page and/or on different pages. Consequently, it is our opinion that any kind of source location information should be accompanied by the source or linked by an intermediary service such as the Data Abstraction Assistant (DAA). |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 13% |
| Student > Master | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Lecturer | 1 | 7% |
| Researcher | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 6 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 33% |
| Nursing and Health Professions | 2 | 13% |
| Computer Science | 1 | 7% |
| Engineering | 1 | 7% |
| Unknown | 6 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 05 February 2018.
All research outputs
#20,933,010
of 25,711,194 outputs
Outputs from Systematic Reviews
#1,992
of 2,247 outputs
Outputs of similar age
#346,542
of 452,551 outputs
Outputs of similar age from Systematic Reviews
#55
of 55 outputs
Altmetric has tracked 25,711,194 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,247 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
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 452,551 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.