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Mendeley readers
Attention Score in Context
| Title |
Developing optimal search strategies for detecting clinically sound and relevant causation studies in EMBASE
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, March 2005
|
| DOI | 10.1186/1472-6947-5-8 |
| Pubmed ID | |
| Authors |
R Brian Haynes, Monika Kastner, Nancy L Wilczynski, the Hedges Team |
| Abstract |
Evaluating the existence and strength of an association between a putative cause and adverse clinical outcome is complex and best done by assessing all available evidence. With the increasing burden of chronic disease, greater time demands on health professionals, and the explosion of information, effective retrieval of best evidence has become both more important and more difficult. Optimal search retrieval can be hampered by a number of obstacles, especially poor search strategies, but using empirically tested methodological search filters can enhance the accuracy of searches for sound evidence concerning etiology. Although such filters have previously been developed for studies of relevance to causation in MEDLINE, no empirically tested search strategy exists for EMBASE. |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 4 | 5% |
| Brazil | 2 | 2% |
| United Kingdom | 2 | 2% |
| Peru | 1 | 1% |
| Canada | 1 | 1% |
| Nigeria | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 69 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 21% |
| Librarian | 9 | 11% |
| Student > Master | 9 | 11% |
| Student > Doctoral Student | 7 | 9% |
| Professor > Associate Professor | 7 | 9% |
| Other | 19 | 23% |
| Unknown | 13 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 36 | 44% |
| Nursing and Health Professions | 4 | 5% |
| Psychology | 4 | 5% |
| Computer Science | 3 | 4% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 12 | 15% |
| Unknown | 20 | 25% |
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 16 December 2014.
All research outputs
#15,312,760
of 22,774,233 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,309
of 1,984 outputs
Outputs of similar age
#53,017
of 59,491 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 4 outputs
Altmetric has tracked 22,774,233 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 1,984 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% 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 59,491 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.