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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A qualitative analysis of a consensus process to develop quality indicators of injury care
|
|---|---|
| Published in |
Implementation Science, April 2013
|
| DOI | 10.1186/1748-5908-8-45 |
| Pubmed ID | |
| Authors |
Niklas Bobrovitz, Julia S Parrilla, Maria Santana, Sharon E Straus, Henry T Stelfox |
| Abstract |
Consensus methodologies are often used to create evidence-based measures of healthcare quality because they incorporate both available evidence and expert opinion to fill gaps in the knowledge base. However, there are limited studies of the key domains that are considered during panel discussion when developing quality indicators. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Spain | 1 | 2% |
| Unknown | 47 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 18% |
| Student > Master | 9 | 18% |
| Student > Ph. D. Student | 7 | 14% |
| Professor | 3 | 6% |
| Student > Bachelor | 3 | 6% |
| Other | 12 | 24% |
| Unknown | 7 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 38% |
| Nursing and Health Professions | 7 | 14% |
| Social Sciences | 5 | 10% |
| Computer Science | 2 | 4% |
| Business, Management and Accounting | 2 | 4% |
| Other | 7 | 14% |
| Unknown | 8 | 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 19 April 2013.
All research outputs
#15,270,134
of 22,707,247 outputs
Outputs from Implementation Science
#1,553
of 1,721 outputs
Outputs of similar age
#123,426
of 197,463 outputs
Outputs of similar age from Implementation Science
#33
of 35 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 5th percentile – i.e., 5% 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 197,463 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 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.