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Mining rich health data from Canadian physician claims: features and face validity

Overview of attention for article published in BMC Research Notes, October 2014
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  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
2 X users

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Mining rich health data from Canadian physician claims: features and face validity
Published in
BMC Research Notes, October 2014
DOI 10.1186/1756-0500-7-682
Pubmed ID
Authors

Ceara Tess Cunningham, Pin Cai, David Topps, Lawrence W Svenson, Nathalie Jetté, Hude Quan

Abstract

Physician claims data are one of the largest sources of coded health information unique to Canada. There is skepticism from data users about the quality of this data. This study investigated features of diagnostic codes used in the Alberta physician claims database.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 2%
Canada 1 2%
Unknown 46 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Master 8 17%
Student > Ph. D. Student 7 15%
Student > Doctoral Student 2 4%
Student > Bachelor 2 4%
Other 7 15%
Unknown 11 23%
Readers by discipline Count As %
Medicine and Dentistry 15 31%
Nursing and Health Professions 6 13%
Mathematics 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Economics, Econometrics and Finance 2 4%
Other 5 10%
Unknown 15 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 02 October 2014.
All research outputs
#14,659,293
of 22,765,347 outputs
Outputs from BMC Research Notes
#2,090
of 4,262 outputs
Outputs of similar age
#137,999
of 253,597 outputs
Outputs of similar age from BMC Research Notes
#67
of 140 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 50% 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 253,597 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.