↓ Skip to main content

Paramedic literature search filters: optimised for clinicians and academics

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 1,703)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

twitter
85 tweeters

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
58 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Paramedic literature search filters: optimised for clinicians and academics
Published in
BMC Medical Informatics and Decision Making, October 2017
DOI 10.1186/s12911-017-0544-z
Pubmed ID
Authors

Alexander Olaussen, William Semple, Alaa Oteir, Paula Todd, Brett Williams

Abstract

Search filters aid clinicians and academics to accurately locate literature. Despite this, there is no search filter or Medical Subject Headings (MeSH) term pertaining to paramedics. Therefore, the aim of this study was to create two filters to meet to different needs of paramedic clinicians and academics. We created a gold standard from a reference set, which we measured against single terms and search filters. The words and phrases used stemmed from selective exclusion of terms from the previously published Prehospital Search Filter 2.0 as well as a Delphi session with an expert panel of paramedic researchers. Independent authors deemed articles paramedic-relevant or not following an agreed definition. We measured sensitivity, specificity, accuracy and number needed to read (NNR). We located 2102 articles of which 431 (20.5%) related to paramedics. The performance of single terms was on average of high specificity (97.1% (Standard Deviation 7.4%), but of poor sensitivity (12.0%, SD 18.7%). The NNR ranged from 1 to 8.6. The sensitivity-maximising search filter yielded 98.4% sensitivity, with a specificity of 74.3% and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and thus a NNR of 1.48. We have created the first two paramedic specific search filters, one optimised for sensitivity and one optimised for specificity. The sensitivity-maximising search filter yielded 98.4% sensitivity, and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and a NNR of 1.48. A paramedic MeSH term is needed.

Twitter Demographics

The data shown below were collected from the profiles of 85 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 17%
Researcher 6 10%
Lecturer > Senior Lecturer 6 10%
Student > Bachelor 5 9%
Student > Master 5 9%
Other 14 24%
Unknown 12 21%
Readers by discipline Count As %
Nursing and Health Professions 19 33%
Medicine and Dentistry 14 24%
Arts and Humanities 3 5%
Computer Science 2 3%
Social Sciences 2 3%
Other 3 5%
Unknown 15 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 62. 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 June 2021.
All research outputs
#458,453
of 19,021,597 outputs
Outputs from BMC Medical Informatics and Decision Making
#10
of 1,703 outputs
Outputs of similar age
#12,221
of 291,415 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 19,021,597 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,703 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 99% 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 291,415 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them