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Testing feedback message framing and comparators to address prescribing of high-risk medications in nursing homes: protocol for a pragmatic, factorial, cluster-randomized trial

Overview of attention for article published in Implementation Science, July 2017
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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Title
Testing feedback message framing and comparators to address prescribing of high-risk medications in nursing homes: protocol for a pragmatic, factorial, cluster-randomized trial
Published in
Implementation Science, July 2017
DOI 10.1186/s13012-017-0615-7
Pubmed ID
Authors

Noah M. Ivers, Laura Desveaux, Justin Presseau, Catherine Reis, Holly O. Witteman, Monica K. Taljaard, Nicola McCleary, Kednapa Thavorn, Jeremy M. Grimshaw

Abstract

Audit and feedback (AF) interventions that leverage routine administrative data offer a scalable and relatively low-cost method to improve processes of care. AF interventions are usually designed to highlight discrepancies between desired and actual performance and to encourage recipients to act to address such discrepancies. Comparing to a regional average is a common approach, but more recipients would have a discrepancy if compared to a higher-than-average level of performance. In addition, how recipients perceive and respond to discrepancies may depend on how the feedback itself is framed. We aim to evaluate the effectiveness of different comparators and framing in feedback on high-risk prescribing in nursing homes. This is a pragmatic, 2 × 2 factorial, cluster-randomized controlled trial testing variations in the comparator and framing on the effectiveness of quarterly AF in changing high-risk prescribing in nursing homes in Ontario, Canada. We grouped homes that share physicians into clusters and randomized these clusters into the four experimental conditions. Outcomes will be assessed after 6 months; all primary analyses will be by intention-to-treat. The primary outcome (monthly number of high-risk medications received by each patient) will be analysed using a general linear mixed effects regression model. We will present both four-arm and factorial analyses. With 160 clusters and an average of 350 beds per cluster, assuming no interaction and similar effects for each intervention, we anticipate 90% power to detect an absolute mean difference of 0.3 high-risk medications prescribed. A mixed-methods process evaluation will explore potential mechanisms underlying the observed effects, exploring targeted constructs including intention, self-efficacy, outcome expectations, descriptive norms, and goal prioritization. An economic analysis will examine cost-effectiveness analysis from the perspective of the publicly funded health care system. This protocol describes the rationale and methodology of a trial testing manipulations of theory-informed components of an audit and feedback intervention to determine how to improve an existing intervention and provide generalizable insights for implementation science. NCT02979964.

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X Demographics

The data shown below were collected from the profiles of 40 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 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 123 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 15%
Student > Bachelor 15 12%
Student > Master 14 11%
Student > Doctoral Student 11 9%
Student > Ph. D. Student 8 7%
Other 17 14%
Unknown 39 32%
Readers by discipline Count As %
Medicine and Dentistry 23 19%
Nursing and Health Professions 17 14%
Psychology 8 7%
Business, Management and Accounting 5 4%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Other 19 15%
Unknown 46 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 24 January 2019.
All research outputs
#1,618,735
of 25,205,864 outputs
Outputs from Implementation Science
#296
of 1,791 outputs
Outputs of similar age
#30,879
of 318,336 outputs
Outputs of similar age from Implementation Science
#8
of 36 outputs
Altmetric has tracked 25,205,864 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,791 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has done well, scoring higher than 83% 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 318,336 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 90% of its contemporaries.
We're also able to compare this research output to 36 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.