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Evolving forecasting classifications and applications in health forecasting

Overview of attention for article published in International Journal of General Medicine, May 2012
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Mentioned by

twitter
1 tweeter

Citations

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24 Dimensions

Readers on

mendeley
57 Mendeley
Title
Evolving forecasting classifications and applications in health forecasting
Published in
International Journal of General Medicine, May 2012
DOI 10.2147/ijgm.s31079
Pubmed ID
Authors

Ireneous Soyiri, Ireneous Soyiri, Reidpath

Abstract

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 2%
Canada 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 21%
Student > Ph. D. Student 9 16%
Researcher 9 16%
Other 4 7%
Student > Bachelor 4 7%
Other 13 23%
Unknown 6 11%
Readers by discipline Count As %
Engineering 11 19%
Computer Science 7 12%
Medicine and Dentistry 5 9%
Social Sciences 4 7%
Decision Sciences 4 7%
Other 17 30%
Unknown 9 16%

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 May 2012.
All research outputs
#3,534,034
of 4,506,935 outputs
Outputs from International Journal of General Medicine
#201
of 273 outputs
Outputs of similar age
#58,573
of 75,175 outputs
Outputs of similar age from International Journal of General Medicine
#33
of 39 outputs
Altmetric has tracked 4,506,935 research outputs across all sources so far. This one is in the 2nd percentile – i.e., 2% of other outputs scored the same or lower than it.
So far Altmetric has tracked 273 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% 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 75,175 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 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.