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Detecting referral and selection bias by the anonymous linkage of practice, hospital and clinic data using Secure and Private Record Linkage (SAPREL): case study from the evaluation of the Improved…

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2011
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Mentioned by

twitter
2 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
52 Mendeley
citeulike
1 CiteULike
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Title
Detecting referral and selection bias by the anonymous linkage of practice, hospital and clinic data using Secure and Private Record Linkage (SAPREL): case study from the evaluation of the Improved Access to Psychological Therapy (IAPT) service
Published in
BMC Medical Informatics and Decision Making, October 2011
DOI 10.1186/1472-6947-11-61
Pubmed ID
Authors

Simon de Lusignan, Rob Navarro, Tom Chan, Glenys Parry, Kim Dent-Brown, Tony Kendrick

Abstract

The evaluation of demonstration sites set up to provide improved access to psychological therapies (IAPT) comprised the study of all people identified as having common mental health problems (CMHP), those referred to the IAPT service, and a sample of attenders studied in-depth. Information technology makes it feasible to link practice, hospital and IAPT clinic data to evaluate the representativeness of these samples. However, researchers do not have permission to browse and link these data without the patients' consent.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 10%
Iran, Islamic Republic of 1 2%
Australia 1 2%
Unknown 45 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Student > Master 9 17%
Other 5 10%
Researcher 5 10%
Professor > Associate Professor 4 8%
Other 10 19%
Unknown 7 13%
Readers by discipline Count As %
Medicine and Dentistry 12 23%
Psychology 7 13%
Computer Science 7 13%
Nursing and Health Professions 6 12%
Social Sciences 4 8%
Other 9 17%
Unknown 7 13%

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 16 October 2018.
All research outputs
#13,859,548
of 22,655,397 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,065
of 1,978 outputs
Outputs of similar age
#88,091
of 135,903 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#9
of 16 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 44th percentile – i.e., 44% 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 135,903 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.