The Child Youth and Family reforms announced a week ago are wide-ranging and contain a mixture of potential pros and cons for different populations in contact with the whole child welfare system: by which I mean statutory child protection, the wider domain of NGOs, targeted and universal services, and macro social protections. I offer this post as my first reflections, and (as these reforms provide some hearty discussion topics) look forward to the developing policy debates that will ensue.
Author: Emily Keddell
Lies, damned lies and statistics: so the famous saying goes. The problem is, in the counting of social phenomenon (as opposed to physical entities), the way we choose to count things always reflects underpinning social processes rather than objectively verifiable realities. So, the issue is not so much a matter of calling out ‘lies’, but one of discerning the social priorities and concepts driving the categorisation processes used to sort the things at hand.
The latest child poverty monitor makes for grim reading (Simpson et al., 2015). It shows an increase to 29% of New Zealand children now living in poverty, or nearly a third of all children in this land of milk and honey living below the poverty line. There have been various disclaimers that this measure is inaccurate, that it’s somehow ‘artificial’ as it’s obtained due to the median income and housing costs rising, while the incomes of poorer people remain the same. But that’s the point really – that if median incomes and costs rise, and the incomes of poorer people remain constant, then a greater proportion of those families will be unable to purchase basic necessities. This is poverty.
One of the items included in the scope of the current New Zealand government’s review of the Child, Youth and Family services (CYFS) is this one: ‘The potential role of data analytics, including predictive risk modelling, to identify children and young people in need of care and protection’.
Predictive risk modelling (PRM) is a simple and seductive idea. If we can predict with accuracy who is likely to abuse children before they have done so, then we can target services to those families, fulfilling the dual objectives of preventing harm before it occurs, and being uber efficient with taxpayer dollars. Such seductive ideas, especially in an age where access to the ‘big data’ required to attempt such a proposition is viable, are often worth investigating. Enormous datasets can be mined, a large number of variables can be included, and patterns of particular combinations of risk factors for certain populations can be identified. In the case of the proposed Ministry for Social Development (MSD) PRM tool, however, there a number of issues. In particular, the level of accuracy of the PRM tool is overstated, the data it relies on has serious problems, its use as a practice decision-making tool is minimal, it has significant rights implications, and using it to decide who should be offered preventive services may not be any more effective than the current state of affairs (although to be fair this is difficult to ascertain – but needs to be).
In my first ‘pictures in our heads’ post, I noted that assumptions about how problems and their solutions are to be understood are implicit in policies of all kinds. These assumptions influence how we frame the key issues. Therefore, the changes proposed by the Vulnerable Children’s Act and Children’s Action Plan contain assumptions that shape the way we think about the causes of, and solutions to, child abuse.