Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T22:03:26.296Z Has data issue: false hasContentIssue false
  • English
  • Français

Examining Developmental Adversity and Connectedness in Child Welfare-Involved Children

Published online by Cambridge University Press:  01 June 2018

Erin P. Hambrick Open the ORCID record for Erin P. Hambrick [Opens in a new window] ,
Thomas W. Brawner  and
Bruce D. Perry
Erin P. Hambrick*
Affiliation:
The ChildTrauma Academy, Houston, Texas, USA Department of Psychology, University of Missouri – Kansas City, Kansas City, Missouri, USA
Thomas W. Brawner
Affiliation:
The ChildTrauma Academy, Houston, Texas, USA Center for Research Methods and Data Analysis, University of Kansas, Lawrence, Kansas, USA
Bruce D. Perry
Affiliation:
The ChildTrauma Academy, Houston, Texas, USA Department of Psychiatry, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
*
address for correspondence: Erin P. Hambrick, PhD, Department of Psychology, University of Missouri – Kansas City, 5030 Cherry Street, Room 309, Kansas City, Missouri 64114. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Identifying optimal out-of-home placements for child welfare-involved youth is challenging. Examples of youth recovering within each “out-of-home” placement type (foster, relative, residential) are evident, as are examples of youth who are deteriorating. The heterogeneity in developmental history and current functioning of youth makes blanket policies regarding placement unwise. Examination of developmental heterogeneity and functioning of youth in the welfare system can provide insights about factors influencing outcomes, thereby informing practice, program and policy. We explore whether current relational health (connectedness) promotes positive outcomes for child welfare-involved youth while controlling for developmental risk (history of adverse, and lack of relationally positive, experiences). Clinicians at 19 organisations serving child welfare-involved youth used a neurodevelopmentally informed approach to intervention, the Neurosequential Model of Therapeutics (NMT), which includes metrics to assess the developmental timing of children's risk, “connectedness” and neurodevelopmental functioning (e.g., sleep, arousal, cortical control). Data-driven statistical techniques were used to produce stable, generalisable estimates. Risk during the perinatal (0–2 months) period significantly predicted children's functioning; current relational health predicted outcomes more strongly. Although early life developmental risk has a persistent effect on functioning, relationally supportive contexts may mitigate this risk. Improving relational contexts of child welfare-involved youth, regardless of placement type, is key.

Keywords

child traumachild maltreatmentsocial supportneurosequential modelregularisation
Type
Articles
Information
Children Australia , Volume 43 , Special Issue 2: Understanding Outcomes for Care Experienced Children , June 2018 , pp. 105 - 115
Copyright
Copyright © The Author(s) 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aarons, G. A., James, S., Monn, A. R., Raghavan, R., Wells, R., & Leslie, L. (2010). Disentangling the relationship between behaviour problems and placement change in child welfare: A multi-level cross-lag modeling approach. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 70–80.Google Scholar
Ainsworth, F., & Hansen, P. (2014). Family foster care: Can it survive the evidence? Children Australia, 39 (2), 87–92. doi: 10.1017/cha.2014.5.Google Scholar
Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., . . . Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256 (3), 174–186. doi: 10.1007/s00406-005-0624-4.Google Scholar
Beeghly, M., Perry, B. D, & Tronick, E. (2016). Self-regulatory processes in early development. In Maltzman, S. (Ed.), The Oxford handbook of treatment processes and outcomes in psychology: A multidisciplinary, biopsychosocial approach. Oxford Handbooks Online. Oxford: Oxford University Press. doi: 10.1093/oxfordhb/9780199739134.001.0001/oxfordhb-9780199739134-e-3.Google Scholar
Bellis, M. A., Hardcastle, K., Ford, K., Hughes, K., Ashton, K., Quigg, Z., & Butler, N. (2017). Does continuous trusted adult support in childhood impart life-course resilience against adverse childhood experiences - A retrospective study on adult health-harming behaviours and mental well-being. BMC Psychiatry, 17 (1), 110–121.Google Scholar
Bellis, M. A., Hughes, K., Leckenby, N., Hardcastle, K. A., Perkins, C., & Lowey, H. (2015). Measuring mortality and the burden of adult disease associated with adverse childhood experiences in England: A national survey. Journal of Public Health (Oxford, England), 37 (3), 445–454. doi: 10.1093/pubmed/fdu065.Google Scholar
Berens, A. E., Jensen, S. K. G., & Nelson, C. A. III. (2017). Biological embedding of childhood adversity: From physiological mechanisms to clinical implications. BMC Medicine, 15, 135–147.Google Scholar
Bollen, K. A., & Bauldry, S. (2011). Three C's in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16, 265–284.Google Scholar
Cho, B., & Jackson, Y. (2016). Self-reported and case file maltreatment: Relations to psychosocial outcomes for youth in foster care. Children and Youth Services Review, 69, 241–247.Google Scholar
Dai, W., Chen, L., Tan, H., Wang, J., Lai, Z., Kaminga, A. C., . . . Liu, A. (2016). Association between social support and recovery from post-traumatic stress disorder after flood: A 13–14 year follow-up study in Hunan, China. BMC Public Health, 16 (1), 194.Google Scholar
Dunn, E. C., Nishimi, K., Powers, A., & Bradley, B. (2017). Is developmental timing of trauma exposure associated with depressive and post-traumatic stress disorder symptoms in adulthood? Journal of Psychiatric Research, 84, 119–127. doi: 10.1016/j.jpsychires.2016.09.004.Google Scholar
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–185. doi: 10.1080/01621459.1987.10478410.Google Scholar
Fang, X., Brown, D. S., Florence, C. S., & Mercy, J. A. (2012). The economic burden of child maltreatment in the united states and implications for prevention. Child Abuse & Neglect, 36 (2), 156–165. doi: 10.1016/j.chiabu.2011.10.006.Google Scholar
Fox, J. (2008). Applied regression analysis and generalized linear models (2nd ed.). Thousand Oaks, CA: Sage Publications.Google Scholar
Greenhoot, A. (2013). Retrospective methods in developmental science. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (1st ed., pp. 196–210). Guilford Press: New York, New York.Google Scholar
Hambrick, E. P., Brawner, T. W., Perry, B. D., Wang, E., Griffin, G., DeMarco, T., & Poales, C. (2018). Restraint and critical incident reduction following introduction of the Neurosequential Model of Therapeutics (NMT). Residential Treatment for Children & Youth, 35 (1), 2–23.Google Scholar
Hambrick, E. P., Oppenheim-Weller, S., N'zi, A. M., & Taussig, H. N. (2016). Mental health interventions for children in foster care: A systematic review. Children and Youth Services Review, 70, 65–77. doi: 10.1016/j.childyouth.2016.09.002.Google Scholar
Hambrick, E. P., Tunno, A. M., Gabrielli, J., Jackson, Y., & Belz, C. (2014). Using multiple informants to assess child maltreatment: Concordance between case file and youth self-report. Journal of Aggression, Maltreatment & Trauma, 23 (7), 751–771. doi: 10.1080/10926771.2014.933463.Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.Google Scholar
Jackson, A., Frederico, M., Hameed, M., Cox, A., & Kascamanidis, M. (2016). Building a research and practice interface using NMT and Outcome measures. 2nd International Neurosequential Model Symposium, Alberta, Canada.Google Scholar
Johnson, M. H. (2001). Functional brain development in humans. Nature Reviews Neuroscience, 2 (7), 475–483. doi: 10.1038/35081509.Google Scholar
Jonson-Reid, M., Kohl, P. L., & Drake, B. (2012). Child and adult outcomes of chronic child maltreatment. Pediatrics, 129 (5), 839–845. doi: 10.1542/peds.2011-2529.Google Scholar
Ludy-Dobson, C., & Perry, B. D. (2010). The role of healthy relational interactions in buffering the impact of childhood trauma. In Gil, E. (Ed.), Working with children to heal interpersonal trauma, (pp. 26–44). New York: Guilford.Google Scholar
Lupien, S. J., Gunnar, M. R., McEwen, B. S., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10 (6), 434–445. doi: 10.1038/nrn2639.Google Scholar
Manly, J. T., Kim, J. E., Rogosch, F. A., & Cicchetti, D. (2001). Dimensions of child maltreatment and children's adjustment: Contributions of developmental timing and subtype. Development and Psychopathology, 13 (4), 759–782.Google Scholar
McDermott, J. M., Troller-Renfree, S., Vanderwert, R., Nelson, C. A., Zeanah, C. H., & Fox, N. A. (2013). Psychosocial deprivation, executive functions, and the emergence of socio-emotional behavior problems. Frontiers in Human Neuroscience, 7, 167.Google Scholar
McGowan, B. (2005). Historical evolution of child welfare services. In Mallon, G. & Hess, P. (Eds.), Child welfare for the twenty-first Century: A handbook of practices, policies, and programs (pp. 10–46). New York: Columbia University Press.Google Scholar
McSherry, D., & Fargas Malet, M. (2017). Family foster care: Let's not throw the baby out with the bathwater. Children Australia, 42 (3), 217–221. doi: 10.1017/cha.2017.23.Google Scholar
Ogle, C. M., Rubin, D. C., & Siegler, I. C. (2013). The impact of the developmental timing of trauma exposure on PTSD symptoms and psychosocial functioning among older adults. Developmental Psychology, 49 (11), 2191–2200. doi: 10.1037/a0031985.Google Scholar
Perry, B. D. (2001). The neuroarcheology of childhood maltreatment: The neurodevelopmental costs of adverse childhood events. In Franey, K., Geffner, R., & Falconer, R. (Eds.), The cost of maltreatment: Who pays? We all do (pp. 15–37). San Diego: Family Violence and Sexual Assault Institute.Google Scholar
Perry, B. D. (2006). The neurosequential model of therapeutics: Applying principles of neuroscience to clinical work with traumatized and maltreated children. In Webb, N. B. (Ed.), Working with traumatized youth in child welfare (pp. 27–52). New York: The Guilford Press.Google Scholar
Perry, B. D. (2009). Examining child maltreatment through a neurodevelopmental lens: Clinical application of the neurosequential model of therapeutics. Journal of Loss and Trauma, 14 (4), 240–255. doi: 10.1080/15325020903004350.Google Scholar
Perry, B. D., & Dobson, C. (2013). Application of the neurosequential model (NMT) in maltreated children. In Ford, J. & Courtois, C. (Eds.), Treating complex traumatic stress disorders in children and adolescents (pp. 249–260). New York: Guilford Press.Google Scholar
Perry, B. D., & Hambrick, E. (2008). The neurosequential model of therapeutics. Reclaiming Children and Youth, 17 (3), 38–43.Google Scholar
Perry, B. D., & Hambrick, E., & Perry, R. D. (2016). A neurodevelopmental perspective and clinical challenges. In Fong, R. & McRoy, R. (Eds.), Transracial and intercountry adoptions: Culturally sensitive guidance for professionals (pp. 126–153), New York: Columbia University Press.Google Scholar
Perry, B. D., Pollard, R., Blakely, T., William, B. L., & Vigilante, D. (1995). Childhood trauma, the neurobiology of adaptation and ‘use-dependent’ development of the brain: How ‘states’ becomes ‘traits’. Infant Mental Health Journal, 16 (4), 271–291.Google Scholar
Raby, K. L., Roisman, G. I., Labella, M. H., Martin, J., Chris Fraley, R., & Simpson, J. A. (2018). The legacy of early abuse and neglect for social and academic competence from childhood to adulthood. Child Development. https://onlinelibrary.wiley.com/doi/abs/10.1111/cdev.13033.Google Scholar
Schumm, J. A., Briggs-Phillips, M., & Hobfoll, S. E. (2006). Cumulative interpersonal traumas and social support as risk and resiliency factors in predicting PTSD and depression among inner-city women. Journal of Traumatic Stress, 19 (6), 825–836. doi: 10.1002/jts.20159.Google Scholar
Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55 (1), 17–29. doi: 10.1111/j.1467-8624.1984.tb00271.x.Google Scholar
Tronick, E., & Perry, B. D. (2015). The multiple levels of meaning making: The first principles of changing meanings in development and therapy. In Marlock, G., Weiss, H., Young, C., & Soth, M. (Eds.), Handbook of body therapy and somatic psychology (pp. 345–355). Berkeley, CA: North Atlantic Books.Google Scholar
Zarnegar, Z., Hambrick, E. P., Perry, B. D., Azen, S. J., & Peterson, C. (2016). Clinical improvements in adopted children with fetal alcohol spectrum disorders through neurodevelopmentally-informed clinical intervention: A pilot study. Clinical Child Psychology and Psychiatry, 21, 551–567. doi: 10.1177/1359104516636438.Google Scholar