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Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series

Overview of attention for article published in Implementation Science, January 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series
Published in
Implementation Science, January 2018
DOI 10.1186/s13012-017-0688-3
Pubmed ID
Authors

Simon Lewin, Andrew Booth, Claire Glenton, Heather Munthe-Kaas, Arash Rashidian, Megan Wainwright, Meghan A. Bohren, Özge Tunçalp, Christopher J. Colvin, Ruth Garside, Benedicte Carlsen, Etienne V. Langlois, Jane Noyes

Abstract

The GRADE-CERQual ('Confidence in the Evidence from Reviews of Qualitative research') approach provides guidance for assessing how much confidence to place in findings from systematic reviews of qualitative research (or qualitative evidence syntheses). The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation. Confidence in the evidence from qualitative evidence syntheses is an assessment of the extent to which a review finding is a reasonable representation of the phenomenon of interest. CERQual provides a systematic and transparent framework for assessing confidence in individual review findings, based on consideration of four components: (1) methodological limitations, (2) coherence, (3) adequacy of data, and (4) relevance. A fifth component, dissemination (or publication) bias, may also be important and is being explored. As with the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach for effectiveness evidence, CERQual suggests summarising evidence in succinct, transparent, and informative Summary of Qualitative Findings tables. These tables are designed to communicate the review findings and the CERQual assessment of confidence in each finding. This article is the first of a seven-part series providing guidance on how to apply the CERQual approach. In this paper, we describe the rationale and conceptual basis for CERQual, the aims of the approach, how the approach was developed, and its main components. We also outline the purpose and structure of this series and discuss the growing role for qualitative evidence in decision-making. Papers 3, 4, 5, 6, and 7 in this series discuss each CERQual component, including the rationale for including the component in the approach, how the component is conceptualised, and how it should be assessed. Paper 2 discusses how to make an overall assessment of confidence in a review finding and how to create a Summary of Qualitative Findings table. The series is intended primarily for those undertaking qualitative evidence syntheses or using their findings in decision-making processes but is also relevant to guideline development agencies, primary qualitative researchers, and implementation scientists and practitioners.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 661 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 661 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 93 14%
Researcher 92 14%
Student > Master 89 13%
Student > Doctoral Student 36 5%
Student > Bachelor 32 5%
Other 132 20%
Unknown 187 28%
Readers by discipline Count As %
Medicine and Dentistry 128 19%
Nursing and Health Professions 91 14%
Social Sciences 77 12%
Psychology 35 5%
Business, Management and Accounting 16 2%
Other 92 14%
Unknown 222 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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 December 2023.
All research outputs
#1,040,322
of 25,287,709 outputs
Outputs from Implementation Science
#161
of 1,795 outputs
Outputs of similar age
#24,514
of 454,000 outputs
Outputs of similar age from Implementation Science
#8
of 43 outputs
Altmetric has tracked 25,287,709 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,795 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 particularly well, scoring higher than 91% 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 454,000 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 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.