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An overview of realist evaluation for simulation-based education

Overview of attention for article published in Advances in Simulation, July 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#22 of 256)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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74 X users
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1 Facebook page

Citations

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30 Dimensions

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154 Mendeley
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Title
An overview of realist evaluation for simulation-based education
Published in
Advances in Simulation, July 2018
DOI 10.1186/s41077-018-0073-6
Pubmed ID
Authors

Alastair C Graham, Sean McAleer

Abstract

This article describes the key features of realist (realistic) evaluation and illustrates their application using, as an example, a simulation-based course for final year medical students. The use of simulation-based education (SBE) is increasing and so too is the evidence supporting its value as a powerful technique which can lead to substantial educational benefits. Accompanying these changes is a call for research into its use to be more theory-driven and to investigate both 'Did it work?' and as importantly 'Why did it work (or not)?' An evaluation methodology that is capable of answering both questions is realist evaluation. Realist evaluation is an emerging methodology that is suited to evaluating complex interventions such as SBE. The realist philosophy positions itself between positivist and constructivist paradigms and seeks to answer the question 'What works for whom, in what circumstances and why?' In seeking to answer this question, realist evaluation sets out to identify three fundamental components of an intervention, namely context, mechanism and outcome. Educational programmes work (successful outcomes) when theory-driven interventions (mechanisms) are applied to groups under appropriate conditions (context). Realist research uses a mixed methods (qualitative and quantitative) approach to gathering data in order to test the proposed context-mechanism-outcome (CMO) configurations of the intervention under investigation. Realist evaluation offers a valuable methodology for researchers investigating interventions utilising simulation-based education. By investigating and understanding the context, mechanisms and outcomes of SBE interventions, realist evaluation can provide the deeper level of understanding being called for.

X Demographics

X Demographics

The data shown below were collected from the profiles of 74 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 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 154 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 12%
Student > Master 16 10%
Student > Ph. D. Student 12 8%
Student > Postgraduate 11 7%
Student > Doctoral Student 11 7%
Other 42 27%
Unknown 44 29%
Readers by discipline Count As %
Medicine and Dentistry 26 17%
Nursing and Health Professions 23 15%
Social Sciences 18 12%
Psychology 4 3%
Computer Science 4 3%
Other 25 16%
Unknown 54 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 48. 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 31 October 2018.
All research outputs
#835,534
of 24,669,628 outputs
Outputs from Advances in Simulation
#22
of 256 outputs
Outputs of similar age
#16,852
of 301,309 outputs
Outputs of similar age from Advances in Simulation
#2
of 10 outputs
Altmetric has tracked 24,669,628 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 256 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.4. 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 301,309 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 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.