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Matching patients to an intervention for back pain: classifying patients using a latent class approach

Overview of attention for article published in Journal of Evaluation in Clinical Practice, March 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
45 Mendeley
Title
Matching patients to an intervention for back pain: classifying patients using a latent class approach
Published in
Journal of Evaluation in Clinical Practice, March 2014
DOI 10.1111/jep.12115
Pubmed ID
Authors

Martine J. Barons, Frances E. Griffiths, Nick Parsons, Anca Alba, Margaret Thorogood, Graham F. Medley, Sarah E. Lamb

Abstract

Classification of patients with back pain in order to inform treatments is a long-standing aim in medicine. We used latent class analysis (LCA) to classify patients with low back pain and investigate whether different classes responded differently to a cognitive behavioural intervention. The objective was to provide additional guidance on the use of cognitive behavioural therapy to both patients and clinicians.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 2%
United States 1 2%
Unknown 43 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 24%
Student > Ph. D. Student 10 22%
Researcher 9 20%
Other 3 7%
Student > Bachelor 3 7%
Other 9 20%
Readers by discipline Count As %
Medicine and Dentistry 19 42%
Nursing and Health Professions 8 18%
Psychology 7 16%
Agricultural and Biological Sciences 2 4%
Social Sciences 2 4%
Other 7 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 13 November 2014.
All research outputs
#1,023,430
of 12,354,212 outputs
Outputs from Journal of Evaluation in Clinical Practice
#101
of 910 outputs
Outputs of similar age
#19,726
of 196,675 outputs
Outputs of similar age from Journal of Evaluation in Clinical Practice
#3
of 29 outputs
Altmetric has tracked 12,354,212 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 910 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 89% 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 196,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.