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A cross-sectional survey assessing the acceptability and feasibility of self-report electronic data collection about health risks from patients attending an Aboriginal Community Controlled Health…

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
40 Mendeley
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Title
A cross-sectional survey assessing the acceptability and feasibility of self-report electronic data collection about health risks from patients attending an Aboriginal Community Controlled Health Service
Published in
BMC Medical Informatics and Decision Making, April 2014
DOI 10.1186/1472-6947-14-34
Pubmed ID
Authors

Natasha E Noble, Christine L Paul, Mariko L Carey, Robert W Sanson-Fisher, Stephen V Blunden, Jessica M Stewart, Katherine M Conigrave

Abstract

Aboriginal Australians experience significantly worse health and a higher burden of chronic disease than non-Aboriginal Australians. Electronic self-report data collection is a systematic means of collecting data about health risk factors which could help to overcome screening barriers and assist in the provision of preventive health care. Yet this approach has not been tested in an Aboriginal health care setting. Therefore, the aim of this study was to examine the acceptability and feasibility of a health risk questionnaire administered on a touch screen laptop computer for patients attending an Aboriginal Community Controlled Health Service (ACCHS).

Twitter Demographics

The data shown below were collected from the profiles of 3 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 3%
Unknown 39 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 15%
Researcher 6 15%
Student > Master 6 15%
Other 5 13%
Student > Postgraduate 4 10%
Other 8 20%
Unknown 5 13%
Readers by discipline Count As %
Medicine and Dentistry 13 33%
Nursing and Health Professions 6 15%
Psychology 3 8%
Agricultural and Biological Sciences 3 8%
Sports and Recreations 2 5%
Other 4 10%
Unknown 9 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 April 2014.
All research outputs
#7,161,402
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#658
of 1,122 outputs
Outputs of similar age
#84,461
of 192,607 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 35th percentile – i.e., 35% 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 192,607 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
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