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Optimising the community-based approach to healthcare improvement: Comparative case studies of the clinical community model in practice

Overview of attention for article published in Social Science & Medicine, November 2016
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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 (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

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17 X users

Citations

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23 Dimensions

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121 Mendeley
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Title
Optimising the community-based approach to healthcare improvement: Comparative case studies of the clinical community model in practice
Published in
Social Science & Medicine, November 2016
DOI 10.1016/j.socscimed.2016.11.026
Pubmed ID
Authors

Emma-Louise Aveling, Graham Martin, Georgia Herbert, Natalie Armstrong

Abstract

Community-based approaches to healthcare improvement are receiving increasing attention. Such approaches could offer an infrastructure for efficient knowledge-sharing and a potent means of influencing behaviours, but their potential is yet to be optimised. After briefly reviewing challenges to community-based approaches, we describe in detail the clinical community model. Through exploring clinical communities in practice, we seek to identify practical lessons for optimising this community-based approach to healthcare improvement. Through comparative case studies based on secondary analysis, we examine two contrasting examples of clinical communities in practice - the USA-based Michigan Keystone ICU programme, and the UK-based Improving Lung Cancer Outcomes Project. We focus on three main issues. First, both cases were successful in mobilising diverse communities: favourable starting conditions, core teams with personal credibility, reputable institutional backing and embeddedness in wider networks were important. Second, top-down input to organise regular meetings, minimise conflict and empower those at risk of marginalisation helped establish a strong sense of community and reciprocal ties, while intervention components and measures common to the whole community strengthened peer-norming effects. Third, to drive implementation, technical expertise and responsiveness from the core team were important, but so too were 'hard tactics' (e.g. strict limits on local customisation); these were more easily deployed where the intervention was standardised across the community and a strong evidence-base existed. Contrary to the idea of self-organising communities, our cases make clear that vertical and horizontal forces depend on each other synergistically for their effectiveness. We offer practical lessons for establishing an effective balance of horizontal and vertical influences, and for identifying the types of quality problems most amenable to community-based improvement.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 121 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 17%
Student > Bachelor 14 12%
Researcher 13 11%
Other 10 8%
Student > Ph. D. Student 10 8%
Other 24 20%
Unknown 30 25%
Readers by discipline Count As %
Nursing and Health Professions 28 23%
Medicine and Dentistry 21 17%
Social Sciences 10 8%
Computer Science 4 3%
Psychology 4 3%
Other 15 12%
Unknown 39 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 09 January 2017.
All research outputs
#3,816,735
of 26,601,484 outputs
Outputs from Social Science & Medicine
#3,882
of 12,232 outputs
Outputs of similar age
#67,032
of 421,772 outputs
Outputs of similar age from Social Science & Medicine
#58
of 111 outputs
Altmetric has tracked 26,601,484 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,232 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.5. This one has gotten more attention than average, scoring higher than 68% 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 421,772 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 84% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.