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Describing knowledge encounters in healthcare: a mixed studies systematic review and development of a classification

Overview of attention for article published in Implementation Science, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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1 Google+ user

Citations

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Title
Describing knowledge encounters in healthcare: a mixed studies systematic review and development of a classification
Published in
Implementation Science, March 2017
DOI 10.1186/s13012-017-0564-1
Pubmed ID
Authors

Dominic Hurst, Sharon Mickan

Abstract

Implementation science seeks to promote the uptake of research and other evidence-based findings into practice, but for healthcare professionals, this is complex as practice draws on, in addition to scientific principles, rules of thumb and a store of practical wisdom acquired from a range of informational and experiential sources. The aims of this review were to identify sources of information and professional experiences encountered by healthcare workers and from this to build a classification system, for use in future observational studies, that describes influences on how healthcare professionals acquire and use information in their clinical practice. This was a mixed studies systematic review of observational studies. OVID MEDLINE and Embase and Google Scholar were searched using terms around information, knowledge or evidence and sharing, searching and utilisation combined with terms relating to healthcare groups. Studies were eligible if one of the intentions was to identify information or experiential encounters by healthcare workers. Data was extracted by one author after piloting with another. Studies were assessed using the Mixed Methods Appraisal Tool (MMAT). The primary outcome extracted was the information source or professional experience encounter. Similar encounters were grouped together as single constructs. Our synthesis involved a mixed approach using the top-down logic of the Bliss Bibliographic Classification System (BC2) to generate classification categories and a bottom-up approach to develop descriptive codes (or "facets") for each category, from the data. The generic terms of BC2 were customised by an iterative process of thematic content analysis. Facets were developed by using available theory and keeping in mind the pragmatic end use of the classification. Eighty studies were included from which 178 discreet knowledge encounters were extracted. Six classification categories were developed: what information or experience was encountered; how was the information or experience encountered; what was the mode of encounter; from whom did the information originate or with whom was the experience; how many participants were there; and where did the encounter take place. For each of these categories, relevant descriptive facets were identified. We have sought to identify and classify all knowledge encounters, and we have developed a faceted description of key categories which will support richer descriptions and interrogations of knowledge encounters in healthcare research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 83 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 15%
Student > Ph. D. Student 9 11%
Student > Doctoral Student 9 11%
Student > Master 8 10%
Librarian 4 5%
Other 15 18%
Unknown 26 31%
Readers by discipline Count As %
Nursing and Health Professions 13 15%
Medicine and Dentistry 11 13%
Social Sciences 8 10%
Psychology 5 6%
Computer Science 5 6%
Other 11 13%
Unknown 31 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 06 November 2017.
All research outputs
#2,062,696
of 22,959,818 outputs
Outputs from Implementation Science
#460
of 1,722 outputs
Outputs of similar age
#41,816
of 307,966 outputs
Outputs of similar age from Implementation Science
#19
of 47 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,722 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has gotten more attention than average, scoring higher than 73% 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 307,966 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 86% of its contemporaries.
We're also able to compare this research output to 47 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 59% of its contemporaries.