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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Development and validation of an algorithm to recalibrate mental models and reduce diagnostic errors associated with catheter-associated bacteriuria
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2013
|
| DOI | 10.1186/1472-6947-13-48 |
| Pubmed ID | |
| Authors |
Barbara W Trautner, Rupal D Bhimani, Amber B Amspoker, Sylvia J Hysong, Armandina Garza, P Adam Kelly, Velma L Payne, Aanand D Naik |
| Abstract |
Overtreatment of catheter-associated bacteriuria is a quality and safety problem, despite the availability of evidence-based guidelines. Little is known about how guidelines-based knowledge is integrated into clinicians' mental models for diagnosing catheter-associated urinary tract infection (CA-UTI). The objectives of this research were to better understand clinicians' mental models for CA-UTI, and to develop and validate an algorithm to improve diagnostic accuracy for CA-UTI. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| United States | 1 | 33% |
| India | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Members of the public | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 68 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 9 | 13% |
| Student > Ph. D. Student | 9 | 13% |
| Researcher | 9 | 13% |
| Student > Master | 7 | 10% |
| Student > Doctoral Student | 7 | 10% |
| Other | 15 | 22% |
| Unknown | 13 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 38% |
| Nursing and Health Professions | 10 | 14% |
| Engineering | 3 | 4% |
| Psychology | 2 | 3% |
| Economics, Econometrics and Finance | 1 | 1% |
| Other | 10 | 14% |
| Unknown | 17 | 25% |
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 26 May 2013.
All research outputs
#14,168,910
of 22,708,120 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,101
of 1,981 outputs
Outputs of similar age
#112,293
of 197,213 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#24
of 37 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,981 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% 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 197,213 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.