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Process evaluation of the data-driven quality improvement in primary care (DQIP) trial: active and less active ingredients of a multi-component complex intervention to reduce high-risk primary care…

Overview of attention for article published in Implementation Science, 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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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47 X users
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1 Facebook page

Citations

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24 Dimensions

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105 Mendeley
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Title
Process evaluation of the data-driven quality improvement in primary care (DQIP) trial: active and less active ingredients of a multi-component complex intervention to reduce high-risk primary care prescribing
Published in
Implementation Science, January 2017
DOI 10.1186/s13012-016-0531-2
Pubmed ID
Authors

Aileen Grant, Tobias Dreischulte, Bruce Guthrie

Abstract

Two to 4% of emergency hospital admissions are caused by preventable adverse drug events. The estimated costs of such avoidable admissions in England were £530 million in 2015. The data-driven quality improvement in primary care (DQIP) intervention was designed to prompt review of patients vulnerable from currently prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and anti-platelets and was found to be effective at reducing this prescribing. A process evaluation was conducted parallel to the trial, and this paper reports the analysis which aimed to explore response to the intervention delivered to clusters in relation to participants' perceptions about which intervention elements were active in changing their practice. Data generation was by in-depth interview with key staff exploring participant's perceptions of the intervention components. Analysis was iterative using the framework technique and drawing on normalisation process theory. All the primary components of the intervention were perceived as active, but at different stages of implementation: financial incentives primarily supported recruitment; education motivated the GPs to initiate implementation; the informatics tool facilitated sustained implementation. Participants perceived the primary components as interdependent. Intervention subcomponents also varied in whether and when they were active. For example, run charts providing feedback of change in prescribing over time were ignored in the informatics tool, but were motivating in some practices in the regular e-mailed newsletter. The high-risk NSAID and anti-platelet prescribing targeted was accepted as important by all interviewees, and this shared understanding was a key wider context underlying intervention effectiveness. This was a novel use of process evaluation data which examined whether and how the individual intervention components were effective from the perspective of the professionals delivering changed care to patients. These findings are important for reproducibility and roll-out of the intervention. ClinicalTrials.gov, NCT01425502 .

X Demographics

X Demographics

The data shown below were collected from the profiles of 47 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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Norway 1 <1%
Unknown 104 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 18%
Student > Bachelor 11 10%
Researcher 10 10%
Student > Master 10 10%
Student > Doctoral Student 7 7%
Other 14 13%
Unknown 34 32%
Readers by discipline Count As %
Medicine and Dentistry 27 26%
Nursing and Health Professions 10 10%
Psychology 8 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Social Sciences 3 3%
Other 16 15%
Unknown 37 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 02 March 2017.
All research outputs
#1,150,927
of 22,931,367 outputs
Outputs from Implementation Science
#218
of 1,722 outputs
Outputs of similar age
#26,436
of 420,788 outputs
Outputs of similar age from Implementation Science
#8
of 42 outputs
Altmetric has tracked 22,931,367 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th 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 14.7. This one has done well, scoring higher than 87% 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 420,788 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 93% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.