000			01699nab a22002537a 4500
005			20250625151347.0
008			140821s2014 xxu||||| |||| 00| 0 eng d
040			_aAFVC
100			_ade Haan, Irene _93712
245			_aAnother Pandora's box? _bSome pros and cons of predictive risk modeling _cIrene de Haan and Marie Connolly
260			_bElsevier, _c2014
500			_aChildren and Youth Services Review, 2014, 47(1): 86-91
520			_aEarly intervention, promoted as being important to the prevention of child maltreatment, is challenged by the difficulty of identifying at risk families before patterns of abuse are established. A way of identifying these families before they reach the radar of statutory systems of child protection is through predictive risk modeling (PRM). Using large datasets PRM tools are able to use algorithms with significant capacity to ascertain and stratify children's risk of experiencing maltreatment in the future. In the process, however, they also identify families who may well benefit from support but are not on a maltreatment trajectory — the so called ‘false positives’ who would not be among those families later identified as mistreating their children. (from the abstract)
650			_aCHILD PROTECTION _9118
650		0	_aPREDICTIVE RISK MODELLING _94928
650		4	_aSOCIAL SERVICES _9555
651		4	_aNEW ZEALAND _92588
650			_9103 _aCHILD ABUSE
700			_aConnolly, Marie _9951
773	0		_tChildren and Youth Services Review, 2014, 47(1): 86-91
830			_aChildren and Youth Services Review _94699
856			_uhttp://dx.doi.org/10.1016/j.childyouth.2014.07.016 _zAccess the abstract
942			_2ddc _cARTICLE
999			_c4474 _d4474