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Advancing the literature on designing audit and feedback interventions: identifying theory-informed hypotheses

Overview of attention for article published in Implementation Science, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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80 tweeters

Citations

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38 Dimensions

Readers on

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86 Mendeley
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Title
Advancing the literature on designing audit and feedback interventions: identifying theory-informed hypotheses
Published in
Implementation Science, September 2017
DOI 10.1186/s13012-017-0646-0
Pubmed ID
Authors

Heather L. Colquhoun, Kelly Carroll, Kevin W. Eva, Jeremy M. Grimshaw, Noah Ivers, Susan Michie, Anne Sales, Jamie C. Brehaut

Abstract

Audit and feedback (A&F) is a common strategy for helping health providers to implement evidence into practice. Despite being extensively studied, health care A&F interventions remain variably effective, with overall effect sizes that have not improved since 2003. Contributing to this stagnation is the fact that most health care A&F interventions have largely been designed without being informed by theoretical understanding from the behavioral and social sciences. To determine if the trend can be improved, the objective of this study was to develop a list of testable, theory-informed hypotheses about how to design more effective A&F interventions. Using purposive sampling, semi-structured 60-90-min telephone interviews were conducted with experts in theories related to A&F from a range of fields (e.g., cognitive, health and organizational psychology, medical decision-making, economics). Guided by detailed descriptions of A&F interventions from the health care literature, interviewees described how they would approach the problem of designing improved A&F interventions. Specific, theory-informed hypotheses about the conditions for effective design and delivery of A&F interventions were elicited from the interviews. The resulting hypotheses were assigned by three coders working independently into themes, and categories of themes, in an iterative process. We conducted 28 interviews and identified 313 theory-informed hypotheses, which were placed into 30 themes. The 30 themes included hypotheses related to the following five categories: A&F recipient (seven themes), content of the A&F (ten themes), process of delivery of the A&F (six themes), behavior that was the focus of the A&F (three themes), and other (four themes). We have identified a set of testable, theory-informed hypotheses from a broad range of behavioral and social science that suggest conditions for more effective A&F interventions. This work demonstrates the breadth of perspectives about A&F from non-healthcare-specific disciplines in a way that yields testable hypotheses for healthcare A&F interventions. These results will serve as the foundation for further work seeking to set research priorities among the A&F research community.

Twitter Demographics

The data shown below were collected from the profiles of 80 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 19%
Student > Ph. D. Student 13 15%
Other 12 14%
Student > Master 11 13%
Student > Doctoral Student 8 9%
Other 15 17%
Unknown 11 13%
Readers by discipline Count As %
Medicine and Dentistry 23 27%
Nursing and Health Professions 15 17%
Psychology 9 10%
Social Sciences 5 6%
Computer Science 2 2%
Other 11 13%
Unknown 21 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 43. 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 21 June 2020.
All research outputs
#516,121
of 15,882,500 outputs
Outputs from Implementation Science
#100
of 1,501 outputs
Outputs of similar age
#16,087
of 279,460 outputs
Outputs of similar age from Implementation Science
#2
of 5 outputs
Altmetric has tracked 15,882,500 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,501 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. This one has done particularly well, scoring higher than 93% 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 279,460 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 94% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.