Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the 'Black Box' of Machine Learning.
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...
| Publicado en: | British Journal of Social Work Vol. 46; no. 4; pp. 1044 - 1059 |
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| Formato: | Journal Article |
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Oxford University Press / USA
Jun2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116498131&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116498131 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00453102 BHO jtl: British Journal of Social Work issn: 00453102 maglogo: N pubinfo: dt: Jun2016 vid: 46 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 116498131 10.1093/bjsw/bcv031 116498131 ppf: 1044 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the 'Black Box' of Machine Learning. aug: au: Gillingham, Philip affil: School of Social Work and Human Services, University of Queensland, St Lucia Campus, Brisbane, Queensland, Australia sug: subj: Child Abuse Prevention and Control Child Abuse Risk Factors Social Work Child Welfare Risk Assessment New Zealand Algorithms Early Intervention Decision Making ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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