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Determinants of successful clinical networks: the conceptual framework and study protocol

Overview of attention for article published in Implementation Science, March 2012
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Title
Determinants of successful clinical networks: the conceptual framework and study protocol
Published in
Implementation Science, March 2012
DOI 10.1186/1748-5908-7-16
Pubmed ID
Authors

Mary Haines, Bernadette Brown, Jonathan Craig, Catherine D'Este, Elizabeth Elliott, Emily Klineberg, Elizabeth McInnes, Sandy Middleton, Christine Paul, Sally Redman, Elizabeth M Yano

Abstract

Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
Spain 1 1%
United States 1 1%
Belgium 1 1%
Unknown 73 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 22%
Student > Ph. D. Student 14 18%
Student > Master 11 14%
Student > Bachelor 6 8%
Student > Doctoral Student 5 6%
Other 16 20%
Unknown 10 13%
Readers by discipline Count As %
Medicine and Dentistry 28 35%
Nursing and Health Professions 15 19%
Social Sciences 9 11%
Computer Science 3 4%
Business, Management and Accounting 3 4%
Other 9 11%
Unknown 12 15%
Attention Score in Context

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 15 March 2012.
All research outputs
#15,242,707
of 22,663,969 outputs
Outputs from Implementation Science
#1,549
of 1,716 outputs
Outputs of similar age
#100,159
of 156,636 outputs
Outputs of similar age from Implementation Science
#29
of 31 outputs
Altmetric has tracked 22,663,969 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 1,716 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 156,636 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.