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Fitting parametric random effects models in very large data sets with application to VHA national data

Overview of attention for article published in BMC Medical Research Methodology, October 2012
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Mentioned by

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1 tweeter

Citations

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11 Dimensions

Readers on

mendeley
30 Mendeley
Title
Fitting parametric random effects models in very large data sets with application to VHA national data
Published in
BMC Medical Research Methodology, October 2012
DOI 10.1186/1471-2288-12-163
Pubmed ID
Authors

Mulugeta Gebregziabher, Leonard Egede, Gregory E Gilbert, Kelly Hunt, Paul J Nietert, Patrick Mauldin

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
United States 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 50%
Student > Ph. D. Student 5 17%
Professor 2 7%
Librarian 2 7%
Student > Master 2 7%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Medicine and Dentistry 11 37%
Computer Science 3 10%
Mathematics 3 10%
Social Sciences 3 10%
Economics, Econometrics and Finance 3 10%
Other 5 17%
Unknown 2 7%

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 10 October 2019.
All research outputs
#11,189,512
of 14,110,335 outputs
Outputs from BMC Medical Research Methodology
#1,082
of 1,294 outputs
Outputs of similar age
#190,558
of 263,486 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 14,110,335 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,294 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one is in the 8th percentile – i.e., 8% 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 263,486 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them