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Attention Score in Context
| Title |
Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads
|
|---|---|
| Published in |
BMC Public Health, December 2014
|
| DOI | 10.1186/1471-2458-14-1285 |
| Pubmed ID | |
| Authors |
Ingmar Schäfer, Hanna Kaduszkiewicz, Hans-Otto Wagner, Gerhard Schön, Martin Scherer, Hendrik van den Bussche |
| Abstract |
Multimorbidity is highly prevalent in the elderly and relates to many adverse outcomes, such as higher mortality, increased disability and functional decline. Many studies tried to reduce the heterogeneity of multimorbidity by identifying multimorbidity clusters or disease combinations, however, the internal structure of multimorbidity clusters and the linking between disease combinations and clusters are still unknown. The aim of this study was to depict which diseases were associated with each other on person-level within the clusters and which ones were responsible for overlapping multimorbidity clusters. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 1% |
| United States | 2 | <1% |
| Denmark | 1 | <1% |
| France | 1 | <1% |
| Unknown | 194 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 37 | 18% |
| Researcher | 37 | 18% |
| Student > Master | 25 | 12% |
| Student > Doctoral Student | 12 | 6% |
| Professor | 10 | 5% |
| Other | 39 | 19% |
| Unknown | 41 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 67 | 33% |
| Social Sciences | 12 | 6% |
| Psychology | 10 | 5% |
| Computer Science | 10 | 5% |
| Nursing and Health Professions | 9 | 4% |
| Other | 36 | 18% |
| Unknown | 57 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 25 May 2023.
All research outputs
#4,246,851
of 24,187,594 outputs
Outputs from BMC Public Health
#4,713
of 15,942 outputs
Outputs of similar age
#57,059
of 362,842 outputs
Outputs of similar age from BMC Public Health
#59
of 189 outputs
Altmetric has tracked 24,187,594 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,942 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has gotten more attention than average, scoring higher than 70% 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 362,842 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 84% of its contemporaries.
We're also able to compare this research output to 189 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 69% of its contemporaries.