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Mendeley readers
Attention Score in Context
Title |
Individual-level space-time analyses of emergency department data using generalized additive modeling
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Published in |
BMC Public Health, August 2012
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DOI | 10.1186/1471-2458-12-687 |
Pubmed ID | |
Authors |
Verónica M Vieira, Janice M Weinberg, Thomas F Webster |
Abstract |
Although daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can also be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). This paper describes the methods for adapting GAMs so they can be applied to ED data. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 35% |
Student > Master | 4 | 24% |
Researcher | 3 | 18% |
Professor > Associate Professor | 2 | 12% |
Other | 1 | 6% |
Other | 0 | 0% |
Unknown | 1 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 3 | 18% |
Environmental Science | 2 | 12% |
Computer Science | 2 | 12% |
Biochemistry, Genetics and Molecular Biology | 1 | 6% |
Mathematics | 1 | 6% |
Other | 6 | 35% |
Unknown | 2 | 12% |
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 22 August 2012.
All research outputs
#18,313,878
of 22,675,759 outputs
Outputs from BMC Public Health
#12,765
of 14,757 outputs
Outputs of similar age
#129,643
of 169,209 outputs
Outputs of similar age from BMC Public Health
#283
of 322 outputs
Altmetric has tracked 22,675,759 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 14,757 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 6th percentile – i.e., 6% 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 169,209 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 322 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.