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Mendeley readers
Attention Score in Context
Title |
Validating archetypes for the Multiple Sclerosis Functional Composite
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, August 2014
|
DOI | 10.1186/1472-6947-14-64 |
Pubmed ID | |
Authors |
Michael Braun, Alexander Ulrich Brandt, Stefan Schulz, Martin Boeker |
Abstract |
Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 14% |
Australia | 1 | 14% |
United Kingdom | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Practitioners (doctors, other healthcare professionals) | 2 | 29% |
Scientists | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 2% |
Unknown | 59 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 15 | 25% |
Researcher | 9 | 15% |
Student > Ph. D. Student | 9 | 15% |
Student > Postgraduate | 8 | 13% |
Student > Bachelor | 6 | 10% |
Other | 9 | 15% |
Unknown | 4 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 21 | 35% |
Medicine and Dentistry | 11 | 18% |
Engineering | 5 | 8% |
Nursing and Health Professions | 4 | 7% |
Agricultural and Biological Sciences | 3 | 5% |
Other | 10 | 17% |
Unknown | 6 | 10% |
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 08 August 2014.
All research outputs
#6,090,201
of 22,759,618 outputs
Outputs from BMC Medical Informatics and Decision Making
#548
of 1,985 outputs
Outputs of similar age
#57,349
of 229,815 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 30 outputs
Altmetric has tracked 22,759,618 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,985 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 72% 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 229,815 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 74% of its contemporaries.
We're also able to compare this research output to 30 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 73% of its contemporaries.