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Predictive modelling: parents’ decision making to use online child health information to increase their understanding and/or diagnose or treat their child’s health

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
5 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
102 Mendeley
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Title
Predictive modelling: parents’ decision making to use online child health information to increase their understanding and/or diagnose or treat their child’s health
Published in
BMC Medical Informatics and Decision Making, December 2012
DOI 10.1186/1472-6947-12-144
Pubmed ID
Authors

Anne M Walsh, Melissa K Hyde, Kyra Hamilton, Katherine M White

Abstract

The quantum increases in home Internet access and available online health information with limited control over information quality highlight the necessity of exploring decision making processes in accessing and using online information, specifically in relation to children who do not make their health decisions. The aim of this study was to understand the processes explaining parents' decisions to use online health information for child health care.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Switzerland 1 <1%
Unknown 96 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 23%
Student > Ph. D. Student 17 17%
Researcher 15 15%
Student > Postgraduate 9 9%
Student > Bachelor 8 8%
Other 20 20%
Unknown 10 10%
Readers by discipline Count As %
Medicine and Dentistry 26 25%
Nursing and Health Professions 16 16%
Social Sciences 15 15%
Psychology 11 11%
Computer Science 6 6%
Other 16 16%
Unknown 12 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 December 2012.
All research outputs
#3,071,604
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#339
of 1,122 outputs
Outputs of similar age
#61,621
of 261,034 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#52
of 158 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 69% 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 261,034 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 76% of its contemporaries.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.