↓ Skip to main content

What’s in a mechanism? Development of a key concept in realist evaluation

Overview of attention for article published in Implementation Science, April 2015
Altmetric Badge

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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
1 blog
policy
3 policy sources
twitter
64 X users

Citations

dimensions_citation
565 Dimensions

Readers on

mendeley
865 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
What’s in a mechanism? Development of a key concept in realist evaluation
Published in
Implementation Science, April 2015
DOI 10.1186/s13012-015-0237-x
Pubmed ID
Authors

Sonia Michelle Dalkin, Joanne Greenhalgh, Diana Jones, Bill Cunningham, Monique Lhussier

Abstract

The idea that underlying, generative mechanisms give rise to causal regularities has become a guiding principle across many social and natural science disciplines. A specific form of this enquiry, realist evaluation is gaining momentum in the evaluation of complex social interventions. It focuses on 'what works, how, in which conditions and for whom' using context, mechanism and outcome configurations as opposed to asking whether an intervention 'works'. Realist evaluation can be difficult to codify and requires considerable researcher reflection and creativity. As such there is often confusion when operationalising the method in practice. This article aims to clarify and further develop the concept of mechanism in realist evaluation and in doing so aid the learning of those operationalising the methodology. Using a social science illustration, we argue that disaggregating the concept of mechanism into its constituent parts helps to understand the difference between the resources offered by the intervention and the ways in which this changes the reasoning of participants. This in turn helps to distinguish between a context and mechanism. The notion of mechanisms 'firing' in social science research is explored, with discussions surrounding how this may stifle researchers' realist thinking. We underline the importance of conceptualising mechanisms as operating on a continuum, rather than as an 'on/off' switch. The discussions in this article will hopefully progress and operationalise realist methods. This development is likely to occur due to the infancy of the methodology and its recent increased profile and use in social science research. The arguments we present have been tested and are explained throughout the article using a social science illustration, evidencing their usability and value.

X Demographics

X Demographics

The data shown below were collected from the profiles of 64 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 865 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 11 1%
Netherlands 2 <1%
Australia 2 <1%
Kenya 1 <1%
South Africa 1 <1%
France 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Mexico 1 <1%
Other 1 <1%
Unknown 843 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 192 22%
Researcher 148 17%
Student > Master 105 12%
Student > Doctoral Student 55 6%
Other 49 6%
Other 143 17%
Unknown 173 20%
Readers by discipline Count As %
Social Sciences 211 24%
Medicine and Dentistry 122 14%
Nursing and Health Professions 88 10%
Psychology 62 7%
Business, Management and Accounting 45 5%
Other 109 13%
Unknown 228 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 56. 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 29 June 2022.
All research outputs
#770,920
of 25,765,370 outputs
Outputs from Implementation Science
#77
of 1,821 outputs
Outputs of similar age
#8,743
of 263,166 outputs
Outputs of similar age from Implementation Science
#3
of 55 outputs
Altmetric has tracked 25,765,370 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,821 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done particularly well, scoring higher than 95% 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 263,166 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 96% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.