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Using qualitative comparative analysis in a systematic review of a complex intervention

Overview of attention for article published in Systematic Reviews, May 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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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2 policy sources
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16 X users

Citations

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35 Dimensions

Readers on

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102 Mendeley
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2 CiteULike
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Title
Using qualitative comparative analysis in a systematic review of a complex intervention
Published in
Systematic Reviews, May 2016
DOI 10.1186/s13643-016-0256-y
Pubmed ID
Authors

Leila Kahwati, Sara Jacobs, Heather Kane, Megan Lewis, Meera Viswanathan, Carol E. Golin

Abstract

Systematic reviews evaluating complex interventions often encounter substantial clinical heterogeneity in intervention components and implementation features making synthesis challenging. Qualitative comparative analysis (QCA) is a non-probabilistic method that uses mathematical set theory to study complex phenomena; it has been proposed as a potential method to complement traditional evidence synthesis in reviews of complex interventions to identify key intervention components or implementation features that might explain effectiveness or ineffectiveness. The objective of this study was to describe our approach in detail and examine the suitability of using QCA within the context of a systematic review. We used data from a completed systematic review of behavioral interventions to improve medication adherence to conduct two substantive analyses using QCA. The first analysis sought to identify combinations of nine behavior change techniques/components (BCTs) found among effective interventions, and the second analysis sought to identify combinations of five implementation features (e.g., agent, target, mode, time span, exposure) found among effective interventions. For each substantive analysis, we reframed the review's research questions to be designed for use with QCA, calibrated sets (i.e., transformed raw data into data used in analysis), and identified the necessary and/or sufficient combinations of BCTs and implementation features found in effective interventions. Our application of QCA for each substantive analysis is described in detail. We extended the original review findings by identifying seven combinations of BCTs and four combinations of implementation features that were sufficient for improving adherence. We found reasonable alignment between several systematic review steps and processes used in QCA except that typical approaches to study abstraction for some intervention components and features did not support a robust calibration for QCA. QCA was suitable for use within a systematic review of medication adherence interventions and offered insights beyond the single dimension stratifications used in the original completed review. Future prospective use of QCA during a review is needed to determine the optimal way to efficiently integrate QCA into existing approaches to evidence synthesis of complex interventions.

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X Demographics

The data shown below were collected from the profiles of 16 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 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 101 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 23%
Researcher 17 17%
Student > Master 14 14%
Student > Doctoral Student 7 7%
Student > Postgraduate 6 6%
Other 17 17%
Unknown 18 18%
Readers by discipline Count As %
Social Sciences 17 17%
Nursing and Health Professions 12 12%
Medicine and Dentistry 11 11%
Business, Management and Accounting 10 10%
Psychology 8 8%
Other 19 19%
Unknown 25 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 14 December 2022.
All research outputs
#2,397,635
of 25,292,378 outputs
Outputs from Systematic Reviews
#401
of 2,217 outputs
Outputs of similar age
#37,052
of 305,372 outputs
Outputs of similar age from Systematic Reviews
#8
of 35 outputs
Altmetric has tracked 25,292,378 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 2,217 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has done well, scoring higher than 81% 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 305,372 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 87% of its contemporaries.
We're also able to compare this research output to 35 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.