Decisions to Protect Children: A Decision Making Ecology

  • John D. Fluke
  • Donald J. Baumann
  • Len I. Dalgleish
  • Homer D. Kern
Chapter
Part of the Child Maltreatment book series (MALT, volume 2)

Abstract

Decisions in child protection and child welfare, for example removal decisions, are made under uncertainty. This chapter provides an overview of decision making theory and research focused on improving decision making in child welfare, and the limitations of our current approaches. The framework developed in the chapter, The Decision Making Ecology (DME) and General Assessment and Decision Making (GADM) model considers child welfare decisions to be a function of case (e.g., type and severity of maltreatment, risk, poverty), decision maker (e.g., experience, values), organizational (e.g., policy, workload, resources), and external characteristics (e.g., critical events, funding). Research has shown that although workers attend to case information similarly to arrive at an assessment, the factors determining their willingness to take action vary; the General Assessment and Decision Making (GADM). Workers, supervisors, administrators, and judges reach individual decision thresholds where assessment information resulting in action in combination with competing views of consequences, including disparities. The chapter concludes with applications of the framework and prospects for improving decision making.

Keywords

Child Welfare Psychological Process Prospect Theory Child Protection Decision Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Baumann, D. J., Kern, H., & Fluke, J. D. (1997a). Foundations of the decision making ecology and overview. In H. D. Kern, Baumann, D. J., & Fluke, J. D. (Eds.), Worker Improvements to the Decision and Outcome Model (WISDOM): The child welfare decision enhancement project. Washington, DC: The Children’s Bureau.Google Scholar
  2. Baumann, D. J., Kern, H. D., McFadden, T., & Law, J. R. (1997b). Individual and organizational factors in burnout and turnover: A decision making ecology approach. In H. D. Kern, D. J. Baumann, & J. D. Fluke (Eds.), Worker Improvements to the Decision and Outcome Model (WISDOM): The child welfare decision enhancement project. Washington, DC: The Children’s Bureau.Google Scholar
  3. Baumann, D. J., Law, R., Sheets, J. H., Reid, G., & Graham, J. C. (2005). Evaluating the effectiveness of actuarial risk assessment models. Children and Youth Services Review, 27, 465–490.CrossRefGoogle Scholar
  4. Baumann, D. J., Fluke, J., Graham, J. C., Wittenstrom, K., Hedderson, J., Riveau, S., Detlaff, A., Rycraft, J., Ortiz, M. J., James, J., Kromrei, L., Craig, S., Capouch, Sheets, J., Ward, D., Breidenbach, R., Hardaway, A., Boudreau, B., & Brown, N. (2010, March). Disproportionality in child protective services: The preliminary results of Statewide reform efforts. Austin, Texas: Texas Department of Family and Protective Services.Google Scholar
  5. Baumann, D. J., Grigsby, C., Sheets, J., Reid, G., Graham, J. C., Robinson, D., Holoubek, J., Farris, J., & Jeffries, V. (2011a). Concept guided risk assessment: Promoting prediction and understanding. Children and Youth Services Review, 33, 1648–1657.CrossRefGoogle Scholar
  6. Baumann, D. J., Dalgleish, L., Fluke, J. D., & Kern, H. D. (2011b). The decision making ecology. Denver: American Humane Association.Google Scholar
  7. Bentler, P. M. (1983). Some contributions to efficient statistics in structural models: Specification and estimation of moment structures. Psychometrika, 48, 493–517.CrossRefGoogle Scholar
  8. Bowers, K. S. (1984). On being unconsciously influenced and informed. In K. S. Bowers & D. Meichenbaum (Eds.), The unconscious reconsidered (pp. 227–272). New York: Wiley.Google Scholar
  9. Dalgleish, L. I. (1988). Decision making in child abuse cases: Applications of social judgment theory and signal detection theory. In B. Brehmer & C. R. B. Joyce (Eds.), Human judgment: The SJT view. North Holland: Elsevier.Google Scholar
  10. Dalgleish, L. I. (2003). Risk, needs and consequences. In M. C. Calder (Ed.), Assessments in child care: A comprehensive guide to frameworks and their use (pp. 86–99). Dorset: Russell House Publishing.Google Scholar
  11. Dalgleish, L. I., & Newton, D. (1996). Reunification: Risk assessment and decision making. Presented at the 11th International Congress on Child Abuse and Neglect, Dublin, August.Google Scholar
  12. Davidson-Arad, B., & Benbenishty, R. (2010). Contribution of child protection workers’ attitudes to their risk assessments and intervention recommendations: A study in Israel. Health & Social Care in the Community, 18(1), 1365–2524.Google Scholar
  13. Dettlaff, A., Rivaux, S., Baumann, D. J., Fluke, J. D., & Rycraft, J. R. (2011). Disentangling substantiation: The influence of race, income, and risk on the substantiation decision in child welfare. Children and Youth Services Review, 33(9), 1630–1637.CrossRefGoogle Scholar
  14. Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 41, 380–417.CrossRefGoogle Scholar
  15. Edwards, W. (1961). Behavioral decision theory. Annual Review of Psychology, 12, 473–498.CrossRefGoogle Scholar
  16. Fluke, J. D., Parry, C., Shapiro, P., Hollinshead, D., Bollenbacher, V., Baumann, D., & Davis-Brown, K. (2001). The dynamics of unsubstantiated reports: A multi-state study – Final report. Denver: American Humane Association.Google Scholar
  17. Fluke, J. D., Chabot, M., Fallon, B., MacLaurin, B., & Blackstock, C. (2010). Placement decisions and disparities among aboriginal groups: An application of the decision-making ecology through multi-level analysis. Child Abuse & Neglect, 34, 57–69.CrossRefGoogle Scholar
  18. Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases”. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology (Vol. 2, pp. 83–115). Chichester: Wiley.Google Scholar
  19. Gigerenzer, G. (1993). The bounded rationality of probabilistic mental models. In K. Manktelow & D. E. Over (Eds.), Rationality (pp. 284–313). London: Routledge.Google Scholar
  20. Gigerenzer, G. (1994). Why the distinction between single-event probabilities and frequencies is relevant for psychology and vice versa. In G. Wright & P. Avion (Eds.), Subjective probability (pp. 129–162). New York: Wiley.Google Scholar
  21. Gigerenzer, G. (1996). On narrow norms and vague heuristics: A reply to Kahneman and Tversky. Psychology Review, 103(3), 592–596.CrossRefGoogle Scholar
  22. Gigerenzer, G. (2005). I think, therefore I err. Social Research, 72, 1–24.Google Scholar
  23. Graham, J. C., Fluke, J. D., Baumann D. J., & Detlaff, A. (2013). The decision-making ecology of placing children in foster care: A structural equation model (in preparation).Google Scholar
  24. Hammond, K. R. (1955). Probabilistic functioning and the clinical method. Psychology Review, 62, 255–262.CrossRefGoogle Scholar
  25. Hammond, K. (1996). Human judgment and social policy. New York: Oxford University Press.Google Scholar
  26. Holder, W., & Corry, M. (1989). The child at risk field system: A family preservation approach to decision-making in child protective services. Charlotte: ACTION for Child Protection.Google Scholar
  27. Hunink, M. G., Glasziou, P., Siegel, J., Weeks, J., Pliskin, J., Elstein, A., & Weinstein, M. (2003). Decision making in health and medicine: Integrating evidence and values. Cambridge: University of Cambridge/Cambridge University Press.Google Scholar
  28. Kahneman, D. (2002). Maps of bounded rationality: A perspective on intuitive judgment and choice. Nobel Prize Lecture. http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2002/kahneman-lecture.html. Accessed 8 Dec 2002.
  29. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.CrossRefGoogle Scholar
  30. Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions: A reply to Gigerenzer’s critique. Psychology Review, 103, 582–591.CrossRefGoogle Scholar
  31. Mansell, J., Ota, R., Erasmus, R., & Marks, K. (2011). Reframing child protection: A response to a constant crisis of confidence in child protection. Children and Youth Services Review, 33(11), 2076–2086. doi: 10.1016/j.childyouth.2011.04.019.CrossRefGoogle Scholar
  32. McDonald, G. (1994). Developing empirically-based practice in probation. British Journal of Social Work, 24, 405–427.Google Scholar
  33. McMahon, A. (1998). Damned if you do, damned if you don’t – Working in child welfare. Aldershot: Ashgate.Google Scholar
  34. Meehl, P. E. (1956a). Clinical versus actuarial prediction. In Proceedings of the 1955 Invitational Conference on Testing Problems (pp. 136–141). Princeton: Educational Testing Service.Google Scholar
  35. Meehl, P. E. (1956b). Symposium on clinical and statistical prediction (with C. C. McArthur & D. V. Tiedeman). Journal of Counseling Psychology, 3, 163–173.CrossRefGoogle Scholar
  36. Meehl, P. E. (1956c). Wanted – A good cookbook. American Psychologist, 11, 263–272.CrossRefGoogle Scholar
  37. Monahan, J., & Steadman, H. J. (1996). Violent storms and violent people: How meteorology can inform risk communication in mental health law. American Psychologist, 51(9), 931–938.CrossRefGoogle Scholar
  38. Munro, E. (2005). Improving practice: Child protection as a systems problem. Children and Youth Services Review, 27, 375–391.CrossRefGoogle Scholar
  39. Munro, E. (2011). The Munro review of child protection: Final report, a child centred system. Presented to Parliament by The Secretary of State for Education by Command of Her Majesty. May.Google Scholar
  40. National Research Council. (1989). Improving risk communication. Washington, DC: National Academy Press.Google Scholar
  41. Rivaux, S. L., James, J., Wittenstrom, K., Baumann, D. J., Sheets, J., Henry, J., & Jeffries, V. (2008). The intersection of race, poverty and risk: Understanding the decision to provide services to clients and to remove children. Child Welfare: Special Issue, Racial Disproportionality in Child Welfare, 87(2), 151–168.Google Scholar
  42. Rossi, P. H., Schuerman, J., & Budde, S. (1999). Understanding decisions about child maltreatment. Evaluation Review, 23(6), 599–619.CrossRefGoogle Scholar
  43. Schwab, J., Baumann, D. J., & Gober, K. (1997). Patterns of decision-making. In D. J. Baumann, H. Kern, & J. Fluke (Eds.), Worker Improvements to the Decision and Outcome Model (WISDOM): The child welfare decision enhancement project. Washington, DC: The Children’s Bureau.Google Scholar
  44. Shadish, W. B., Cook, T. D., & Campbell, D. T. (2001). Experimental and Quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.Google Scholar
  45. Shapira, M., & Benbenishty, R. (1993). Modeling judgments and decisions in cases of alleged child abuse and neglect. Social Work Research & Abstracts, 29(2), 14–20.Google Scholar
  46. Sheets, D. (1991). The Texas CARF evaluation. Austin: The Texas Department of Human Services.Google Scholar
  47. Shlonsky, A., & Benbenishty, R. (2013). From evidence to outcomes in child welfare: An international reader. Edited volume in preparation.Google Scholar
  48. Simon, H. (1956). Rational choice and the structure of the environment, 1956. Psychology Review, 29(2), 129–138.Google Scholar
  49. Simon, H. (1959). Theories of decision-making in economics and behavioral sciences. The American Economic Review, XLIX(3), 253–283.Google Scholar
  50. Simon, H. (1972). Theories of bounded rationality. In C. B. McGuire & R. Radner (Eds.), Decision and organization (pp. 161–176). North Holland: North Holland Publishing Company.Google Scholar
  51. Stein, T., & Rzepnicki, T. L. (1983). Decision-making in child welfare intake: A handbook for practitioners. New York: Child Welfare League of America.Google Scholar
  52. Swets, J. A., Tanner, W. P., Jr., & Birdsall, T. G. (1955). Decision processes in perception. Psychology Review, 68, 301–340.CrossRefGoogle Scholar
  53. Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: Some challenges. International Journal of Industrial Engineering: Applications and Practice, 2(1), 35–44.Google Scholar
  54. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle Scholar
  55. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.CrossRefGoogle Scholar
  56. Washington State Department of Social and Health Services. (1986). Washington Risk Assessment Matrix (WRAM). Olympia: WADSHS.Google Scholar
  57. Webb, S. A. (2001). Some considerations on the validity of evidence based practice in social work. British Journal of Social Work, 31, 57–79.CrossRefGoogle Scholar
  58. Weiss, S. M., Kulikowski, C. A., Amarel, S., & Safir, A. (1978). A model-based method for computer-aided medical decision-making. Artificial Intelligence, 11(1–2), 145–172.Google Scholar
  59. Wittenstrom, K., Baumann, D. J., Fluke, J. D., Graham, J. C., & James, J. (2013). The impact of drugs, infants, single mothers, and relatives on reunification: A decision-making ecology approach (in preparation).Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • John D. Fluke
    • 1
  • Donald J. Baumann
    • 2
  • Len I. Dalgleish
    • 3
  • Homer D. Kern
    • 4
  1. 1.Department of Pediatrics, Kempe Center for the Prevention of Child Abuse and NeglectUniversity of Colorado School of Medicine, The Gary Pavilion at Children’s Hospital ColoradoAuroraUSA
  2. 2.Saint Edwards UniversityAustinUSA
  3. 3.University of SterlingScotlandUK
  4. 4.Independent Child Welfare ConsultantChina SpringsUSA

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