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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, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
19 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
47 Mendeley
Title
Optimising the community-based approach to healthcare improvement: Comparative case studies of the clinical community model in practice
Published in
Social Science & Medicine, January 2017
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.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 17%
Researcher 8 17%
Unspecified 7 15%
Student > Bachelor 6 13%
Student > Ph. D. Student 5 11%
Other 13 28%
Readers by discipline Count As %
Unspecified 13 28%
Medicine and Dentistry 12 26%
Social Sciences 6 13%
Nursing and Health Professions 6 13%
Business, Management and Accounting 2 4%
Other 8 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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
#1,221,079
of 12,266,780 outputs
Outputs from Social Science & Medicine
#1,515
of 7,269 outputs
Outputs of similar age
#50,237
of 338,526 outputs
Outputs of similar age from Social Science & Medicine
#51
of 118 outputs
Altmetric has tracked 12,266,780 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,269 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done well, scoring higher than 79% 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 338,526 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 85% of its contemporaries.
We're also able to compare this research output to 118 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 56% of its contemporaries.