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Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health

Overview of attention for article published in BMC Medicine, June 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 (93rd percentile)

Mentioned by

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54 tweeters

Citations

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6 Dimensions

Readers on

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34 Mendeley
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Title
Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health
Published in
BMC Medicine, June 2018
DOI 10.1186/s12916-018-1079-6
Pubmed ID
Authors

Miranda Wolpert, Harry Rutter

Abstract

The use of routinely collected data that are flawed and limited to inform service development in healthcare systems needs to be considered, both theoretically and practically, given the reality in many areas of healthcare that only poor-quality data are available for use in complex adaptive systems. Data may be compromised in a range of ways. They may be flawed, due to missing or erroneously recorded entries; uncertain, due to differences in how data items are rated or conceptualised; proximate, in that data items are a proxy for key issues of concern; and sparse, in that a low volume of cases within key subgroups may limit the possibility of statistical inference. The term 'FUPS' is proposed to describe these flawed, uncertain, proximate and sparse datasets. Many of the systems that seek to use FUPS data may be characterised as dynamic and complex, involving a wide range of agents whose actions impact on each other in reverberating ways, leading to feedback and adaptation. The literature on the use of routinely collected data in healthcare is often implicitly premised on the availability of high-quality data to be used in complicated but not necessarily complex systems. This paper presents an example of the use of a FUPS dataset in the complex system of child mental healthcare. The dataset comprised routinely collected data from services that were part of a national service transformation initiative in child mental health from 2011 to 2015. The paper explores the use of this FUPS dataset to support meaningful dialogue between key stakeholders, including service providers, funders and users, in relation to outcomes of services. There is a particular focus on the potential for service improvement and learning. The issues raised and principles for practice suggested have relevance for other health communities that similarly face the dilemma of how to address the gap between the ideal of comprehensive clear data used in complicated, but not complex, contexts, and the reality of FUPS data in the context of complexity.

Twitter Demographics

The data shown below were collected from the profiles of 54 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 8 24%
Lecturer > Senior Lecturer 5 15%
Student > Postgraduate 4 12%
Unspecified 2 6%
Other 6 18%
Readers by discipline Count As %
Medicine and Dentistry 16 47%
Unspecified 5 15%
Social Sciences 5 15%
Nursing and Health Professions 3 9%
Psychology 2 6%
Other 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 22 May 2019.
All research outputs
#436,121
of 13,396,110 outputs
Outputs from BMC Medicine
#373
of 2,131 outputs
Outputs of similar age
#17,695
of 269,719 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 13,396,110 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 2,131 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.9. This one has done well, scoring higher than 82% 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 269,719 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 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them