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Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning

Overview of attention for article published in British Journal of Social Work, April 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
7 X users

Citations

dimensions_citation
82 Dimensions

Readers on

mendeley
153 Mendeley
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Title
Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning
Published in
British Journal of Social Work, April 2015
DOI 10.1093/bjsw/bcv031
Pubmed ID
Authors

Philip Gillingham

Abstract

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can 'learn', it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the 'black box' of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Australia 1 <1%
Unknown 151 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 14%
Student > Ph. D. Student 20 13%
Student > Master 18 12%
Lecturer 14 9%
Other 9 6%
Other 28 18%
Unknown 43 28%
Readers by discipline Count As %
Social Sciences 55 36%
Computer Science 14 9%
Psychology 11 7%
Economics, Econometrics and Finance 4 3%
Nursing and Health Professions 4 3%
Other 19 12%
Unknown 46 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 April 2022.
All research outputs
#2,202,608
of 25,377,790 outputs
Outputs from British Journal of Social Work
#204
of 2,025 outputs
Outputs of similar age
#27,969
of 279,938 outputs
Outputs of similar age from British Journal of Social Work
#3
of 32 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,025 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has done well, scoring higher than 89% 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 279,938 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 89% of its contemporaries.
We're also able to compare this research output to 32 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 90% of its contemporaries.