Samenvatting
Het doel van deze studie was te onderzoeken of terugkeer naar de jeugdbescherming beter voorspeld wordt door het Licht Instrument Risicotaxatie Kindveiligheid (LIRIK) of door een nieuw te ontwikkelen actuarieel instrument. De steekproef bestond uit 3963 kinderen tussen 0 en 18 jaar (M leeftijd = 9,1 jaar, SD = 5,33) en hun ouder(s) die in een hulpverleningstraject bij Jeugdbescherming Regio Amsterdam (JBRA) zaten vanwege een problematische opgroei- of opvoedingssituatie. Terugkeer was gedefinieerd als het opnieuw starten van een jeugdbeschermingstraject door JBRA. Het Actuarieel Risicotaxatie Instrument voor Jeugdbescherming (ARIJ) werd ontwikkeld middels een CHAID-analyse. Een voorspelling op basis van het LIRIK bleek niet significant beter dan toeval (AUC = .53). De predictieve validiteit van het ARIJ was matig (AUC = .63), maar significant beter dan die van het LIRIK. Veel praktijkinstellingen werken inmiddels met het ARIJ en daarom is het belangrijk te onderzoeken hoe de predictieve validiteit van het ARIJ verder kan worden verbeterd.
Abstract
The aim of this study was to examine whether return to youth protection is better predicted by using the light instrument for risk assessment of child maltreatment (Licht Instrument Risicotaxatie Kindveiligheid, LIRIK) or by using a newly-developed actuarial instrument. The sample consisted of 3963 Dutch children aged between 0 and 18 years (M age = 9.12 years, SD = 5.35) and their parent(s) who were under the supervision of a Dutch child welfare agency (Jeugdbescherming Regio Amsterdam, JBRA) because of problematic child-rearing situations. Relapse was defined as resuming treatment by the JBRA as a result of newly-substantiated problematic child-rearing situations. The actuarial risk assessment instrument for youth protection (Actuarieel Risicotaxatie Instrument voor Jeugdbescherming, ARIJ) was developed by means of a CHAID analysis. The LIRIK performed no better than chance (AUC = 0.53). The predictive validity of the ARIJ was moderate (AUC = 0.63), but performed significantly better than the LIRIK. Further research should focus on how to improve the predictive validity of the ARIJ, particularly since the ARIJ has been implemented by multiple youth care organizations.
Literatuur
Aegisdóttir, S., White, M. J., Spengler, P. M., Maugherman, A. S., Anderson, L. A., Cook, R. S., Rush, J. D., et al. (2006). The meta-analysis of clinical judgment project: Fifty-six years of accumulated research on clinical versus statistical prediction. The Counseling Psychologist, 34(3), 341–382.
Arad-Davidson, B., & Benbenishty, R. (2008). The role of workers’ attitudes and parent and child wishes in child protection workers’ assessments and recommendation regarding removal and reunification. Children and Youth Services Review, 30, 107–121.
Ayoub, C. C., & Milner, J. S. (1985). Failure to thrive: Parental indicators, types, and outcomes. Child Abuse & Neglect, 9(4), 491–499.
Baird, C., & Wagner, D. (2000). The relative validity of actuarial and consensus based risk assessment systems. Children and Youth Services Review, 22, 839–871.
Barber, J. G., Shlonsky, A., Black, T., Goodman, D., & Trocmé, N. (2008). Reliability and predictive validity of a consensus-based risk assessment tool. Journal of Public Child Welfare, 2(2), 173–195.
Barlow, J., Fisher, J. D., & Jones, D. (2010). Systematic review of models of analyzing significant harm. Department of Education. https://www.gov.uk/government/publications/systematic-review-of-models-of-analysing-significant-harm. Geraadpleegd op: 11 november 2013.
Bartelink, C., Kwaadsteniet, L. de, Berge, I. ten, Witteman, C., & Gastel, W. van (2015). Betrouwbaarheid en validiteit van de LIRIK: Eindrapport LIRIK valideringsonderzoek. Utrecht: Nederlands Jeugdinstituut.
Berge, I. J. ten (2008). Instrumenten voor risicotaxatie in situaties van (vermoedelijke) kindermishandeling. Utrecht: Nederlands Jeugdinstituut.
Berge, I. J. ten, & Eijgenraam, K. (2009). Licht Instrument Risicotaxatie Kindveiligheid (LIRIK) [Check List of Child Safety (CLCS). Utrecht: Nederlands Jeugdinstituut.
Busschers, I., Forrer, M., & Dinkgreve, M. (2015). Interbeoordelaarsbetrouwbaarheid Actuarieel Risicotaxatie Instrument voor Jeugdbescherming (ARIJ). Amsterdam: JBRA.
Camasso, M., & Jagannathan, R. (2000). Modeling the reliability and predictive validity of risk assessment in child protective services. Children and Youth Services Review, 22, 873–896.
Cash, S. J. (2001). Risk assessment in child welfare: The art and science. Children and Youth Services Review, 23(11), 811–830.
Chaffin, M., & Valle, L. A. (2003). Dynamic prediction characteristics of the child abuse potential inventory. Child Abuse & Neglect, 27, 463–481.
Dankert, E. W., & Johnson, K. (2014). Risk assessment validation: a prospective study. NCCD Children’s Research Center. http://www.nccdglobal.org/sites/default/files/publication_pdf/risk-assessment-validation.pdf. Geraadpleegd op: 15 juli 2016.
Dawes, R. M. (1994). House of cards: Psychology and psychotherapy built on myth. New York: Free Press.
Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, band 243, pag. 1668–1674).
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44, 837–845.
DePanfilis, D., & Girvin, H. (2005). Investigating child maltreatment in out-of-home care: Barriers to good decision-making. Children & Youth Services Review, 27, 353–374.
DePanfilis, D., & Zuravin, S. (2001). Assessing risk to determine the need for services. Children and Youth Services Review, 23, 3–20.
Dorsey, S., Mustillo, S. A., Farmer, E. M. Z., & Elbogen, E. (2008). Caseworker assessments of risk for recurrent maltreatment: Association with case-specific risk factors and re-reports. Child Abuse & Neglect, 32, 377–391.
D’Andrade, A., Benton, A., & Austin, M. J. (2005). Risk and safety assessment in child welfare: Instrument comparisons. Center for Social Services Research. http://cssr.berkeley.edu/bassc/public/risk_full.pdf. Geraadpleegd op: 8 juli 2016.
Fazel, S., Singh, J. P., Doll, H., & Grann, M. (2012). Use of risk assessment instruments to predict violence and antisocial behavior in 73 samples involving 24.827 people: systematic review and meta-analysis. British Medical Journal, doi:10.1136/bmj.e4692.
Gambrill, E., & Shlonsky, A. (2000). Risk assessment in context. Children and Youth Services Review, 22, 813–837.
Gillingham, P. (2011). Decision-making tools and the development of expertise in child protection practitioners: are we ‘just breeding workers who are good at ticking boxes’? Child & Family Social Work, 16(4), 412–421.
Gillingham, P., & Humphreys, C. (2010). Child protection practitioners and decision-making tools: Observations and reflections from the front line. British Journal of Social Work, 40(8), 2598–2616.
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychology. Public Policy and Law, 2, 293–323.
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis. Psychological assessment, 12(1), 19–30.
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29–36.
Hanson, R. K., & Morton-Bourgon, K. E. (2009). The accuracy of recidivism risk assessments for sexual offenders: a meta-analysis of 118 prediction studies. Psychological Assessment, 21, 1–21.
Hilton, N. Z., Harris, G. T., & Rice, M. E. (2006). Sixty-six years of research on the clinical versus actuarial prediction of violence. The Counseling Psychologist, 34(3), 400–409.
Hindley, N., Ramchandani, P. G., & Jones, D. P. (2006). Risk factors for recurrence of maltreatment: a systematic review. Archives of Disease in Childhood, 91(9), 744–752.
Johnson, W. L. (2011). The validity and utility of the California Family Risk Assessment under practice conditions in the field: a prospective study. Child Abuse & Neglect, 35, 18–28.
Johnson, W., Clancy, T., & Bastian, P. (2015). Child abuse/neglect risk assessment under field practice conditions: Tests of external and temporal validity and comparison with heart disease prediction. Children and Youth Services Review, 56, 76–85.
Kaufmann, E., & Wittmann, W. W. (2016). The success of linear bootstrapping models: Decision domain-, expertise-, and criterion-specific meta-analysis. PLOS ONE, 11(6), e0157914.
Knoke, D., & Trocmé, N. (2005). Reviewing the evidence on assessing risk for child abuse and neglect. Brief Treatment and Crisis Intervention, 5, 310–327.
Leschied, A. W., Chiodo, D., Whitehead, P. C., Hurley, D., & Marshall, L. (2003). The empirical basis of risk assessment in child welfare: the accuracy of risk assessment and clinical judgment. Child Welfare, 82, 527–540.
Luthar, S. S., & Goldstein, A. (2004). Children’s exposure to community violence: Implications for understanding risk and resilience. Journal of Clinical Child and Adolescent Psychology, 33(3), 499–505.
Lyons, P., Doueck, H. J., & Wodarski, J. S. (1996). Risk assessment for child protective services: A review of the empirical literature on instrument performance. Social Work Research, 20, 143–155.
Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Minneapolis: University of Minnesota Press.
Meehl, P. E. (1986). Causes and effects of my disturbing little book. Journal of Personality Assessment, 50, 370–375.
Miller, L. S., Wasserman, G. A., Neugebauer, R., Gorman-Smith, D., & Kamboukos, D. (1999). Witnessed community violence and antisocial behavior in high-risk, urban boys. Journal of Clinical Child Psychology, 28, 2–11.
Milner, J. S., Gold, R. G., Ayoub, C., & Jacewitz, M. M. (1984). Predictive validity of the child abuse potential inventory. Journal of Consulting and Clinical Psychology, 52(5), 879.
Munro, E. (1999). Common errors of reasoning in child protection work. Child Abuse & Neglect, 23, 745–758.
Olver, M. E., Stochdale, K. C., & Wormith, J. S. (2009). Risk assessment with young offenders: A meta-analysis of three assessment measures. Criminal Justice and Behavior, 36, 329–353.
Ondersma, S. J., Chaffin, M. J., Mullins, S. M., & LeBreton, J. M. (2005). A brief form of the Child Abuse Potential Inventory: Development and validation. Journal of ClinicalChild and Adolescent Psychology, 34(2), 301–311.
Pfister, H., & Böhm, G. (2008). The multiplicity of emotions: A framework of emotional functions in decision making. Judgment and Decision Making, 3, 5–17.
Price-Robertson, R., & Bromfield, L. (2011). Risk assessment in child protection. Resource sheet, National Child Protection Clearinghouse, Australian Institute of Family Studies. https://www3.aifs.gov.au/cfca/sites/default/files/publication-documents/rs24.pdf. Geraadpleegd op: 15 juli 2016.
Put, C. E. van der, Assink, M., & Stams, G. J. J. M. (2015). Actuarieel Risicotaxatie Instrument voor Jeugdbescherming (ARIJ). Amsterdam: Universiteit van Amsterdam.
Put, C. E. van der, Hermanns, J., Gelderen, R.-V. L. van, & Sondeijker, F. (2016). Detection of unsafety in families with parental and/or child developmental problems at the start of family support. BMC Psychiatry, 16(1), 1.
Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615–620.
Rosenthal, R. (1991). Meta-analytic procedures for social research. Newbury Park: Sage.
Schwalbe, C. S. (2007). Risk assessment for juvenile justice: a meta-analysis. Law and Human Behavior, 31, 449–462.
Stith, S. M., Liu, T., Davies, L. C., Boykin, E. L., Boykin, E. L., Alder, M. C., Harris, J. M., Dees, J. E. M. E. G., et al. (2009). Risk factors in child maltreatment: A meta-analytic review of the literature. Aggression and Violent Behavior, 14(1), 13–29.
Thomas, S. D., & Leese, M. (2003). Invited editorial. A green-fingered approach can improve the clinical utility of violence risk assessment tools. Criminal Behaviour and Mental Health, 13(3), 153–158.
Vanderbilt-Adriance, E., & Shaw, D. S. (2008). Protective factors and the development of resilience in the context of neighborhood disadvantage. Journal of Abnormal Child Psychology, 36(6), 887–901.
Veenhuizen, H. P. (2013). Risicotaxatie Kindermishandeling (LIRIK): Het effect van een hulpschema als modererende factor. Amsterdam: VU Amsterdam. Master’s thesis
Wald, M. S., & Woolverton, M. (1990). Risk assessment: The emperor’s new clothes? Child Welfare, 69, 483–511.
Dankbetuiging
De auteurs willen graag Marc Dinkgreve, Inge Busschers en Mirte Forrer bedanken voor hun kritische en waardevolle suggesties ten aanzien van het onderzoek. Daarnaast bedanken we Pro Juventute en Jeugdbescherming Regio Amsterdam (JBRA) voor het financieel mogelijk maken van dit onderzoek alsmede voor het beschikbaar stellen van de data die nodig waren voor dit onderzoek.
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Dit artikel is een bewerking van: Put, C. E. van der, Assink, M., & Stams, G. J. J. M (2016). Predicting relapse of problematic child-rearing situations. Children and Youth Services Review 61, 288–295.
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van der Put, C., Assink, M. & Stams, G.J.M. Het voorspellen van problematische opgroei- of opvoedingssituaties. Kind Adolesc 37, 133–154 (2016). https://doi.org/10.1007/s12453-016-0117-4
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DOI: https://doi.org/10.1007/s12453-016-0117-4
Trefwoorden
- risicotaxatie-instrument
- predictieve validiteit
- LIRIK
- problematische opgroei- of opvoedingssituaties
- kindermishandeling
- ARIJ
- jeugdbescherming