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Using latent class analysis to develop a model of the relationship between socioeconomic position and ethnicity: cross-sectional analyses from a multi-ethnic birth cohort study

Overview of attention for article published in BMC Public Health, August 2014
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Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
73 Mendeley
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Title
Using latent class analysis to develop a model of the relationship between socioeconomic position and ethnicity: cross-sectional analyses from a multi-ethnic birth cohort study
Published in
BMC Public Health, August 2014
DOI 10.1186/1471-2458-14-835
Pubmed ID
Authors

Lesley Fairley, Baltica Cabieses, Neil Small, Emily S Petherick, Debbie A Lawlor, Kate E Pickett, John Wright

Abstract

Almost all studies in health research control or investigate socioeconomic position (SEP) as exposure or confounder. Different measures of SEP capture different aspects of the underlying construct, so efficient methodologies to combine them are needed. SEP and ethnicity are strongly associated, however not all measures of SEP may be appropriate for all ethnic groups.

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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 1 1%
Unknown 70 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 22%
Researcher 14 19%
Student > Master 11 15%
Student > Doctoral Student 6 8%
Professor > Associate Professor 5 7%
Other 16 22%
Unknown 5 7%
Readers by discipline Count As %
Medicine and Dentistry 15 21%
Social Sciences 11 15%
Nursing and Health Professions 7 10%
Agricultural and Biological Sciences 6 8%
Psychology 6 8%
Other 20 27%
Unknown 8 11%

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 08 September 2014.
All research outputs
#12,419,649
of 18,893,921 outputs
Outputs from BMC Public Health
#9,554
of 12,493 outputs
Outputs of similar age
#113,548
of 204,665 outputs
Outputs of similar age from BMC Public Health
#1
of 1 outputs
Altmetric has tracked 18,893,921 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,493 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one is in the 16th percentile – i.e., 16% 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 204,665 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 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