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How Qualitative Methods Can be Used to Inform Model Development

Overview of attention for article published in PharmacoEconomics, March 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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13 X users
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1 Facebook page

Citations

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17 Dimensions

Readers on

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63 Mendeley
Title
How Qualitative Methods Can be Used to Inform Model Development
Published in
PharmacoEconomics, March 2017
DOI 10.1007/s40273-017-0499-z
Pubmed ID
Authors

Samantha Husbands, Susan Jowett, Pelham Barton, Joanna Coast

Abstract

Decision-analytic models play a key role in informing healthcare resource allocation decisions. However, there are ongoing concerns with the credibility of models. Modelling methods guidance can encourage good practice within model development, but its value is dependent on its ability to address the areas that modellers find most challenging. Further, it is important that modelling methods and related guidance are continually updated in light of any new approaches that could potentially enhance model credibility. The objective of this article was to highlight the ways in which qualitative methods have been used and recommended to inform decision-analytic model development and enhance modelling practices. With reference to the literature, the article discusses two key ways in which qualitative methods can be, and have been, applied. The first approach involves using qualitative methods to understand and inform general and future processes of model development, and the second, using qualitative techniques to directly inform the development of individual models. The literature suggests that qualitative methods can improve the validity and credibility of modelling processes by providing a means to understand existing modelling approaches that identifies where problems are occurring and further guidance is needed. It can also be applied within model development to facilitate the input of experts to structural development. We recommend that current and future model development would benefit from the greater integration of qualitative methods, specifically by studying 'real' modelling processes, and by developing recommendations around how qualitative methods can be adopted within everyday modelling practice.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 62 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 13%
Student > Master 8 13%
Researcher 7 11%
Student > Doctoral Student 6 10%
Student > Bachelor 3 5%
Other 11 17%
Unknown 20 32%
Readers by discipline Count As %
Social Sciences 8 13%
Medicine and Dentistry 6 10%
Business, Management and Accounting 5 8%
Computer Science 4 6%
Economics, Econometrics and Finance 4 6%
Other 12 19%
Unknown 24 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 18 May 2017.
All research outputs
#4,671,155
of 25,476,463 outputs
Outputs from PharmacoEconomics
#482
of 1,994 outputs
Outputs of similar age
#76,784
of 323,626 outputs
Outputs of similar age from PharmacoEconomics
#9
of 26 outputs
Altmetric has tracked 25,476,463 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,994 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done well, scoring higher than 75% 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 323,626 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 76% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.