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      Beyond Cumulative Risk : A Dimensional Approach to Childhood Adversity

      1 , 2
      Current Directions in Psychological Science
      SAGE Publications

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          Abstract

          Children who have experienced environmental adversity-such as abuse, neglect, or poverty-are more likely to develop physical and mental health problems, perform poorly at school, and have difficulties in social relationships than children who have not encountered adversity. What is less clear is how and why adverse early experiences exert such a profound influence on children's development. Identifying developmental processes that are disrupted by adverse early environments is the key to developing better intervention strategies for children who have experienced adversity. Yet, much existing research relies on a cumulative risk approach that is unlikely to reveal these mechanisms. This approach tallies the number of distinct adversities experienced to create a risk score. This risk score fails to distinguish between distinct types of environmental experience, implicitly assuming that very different experiences influence development through the same underlying mechanisms. We advance an alternative model. This novel approach conceptualizes adversity along distinct dimensions, emphasizes the central role of learning mechanisms, and distinguishes between different forms of adversity that might influence learning in distinct ways. A key advantage of this approach is that learning mechanisms provide clear targets for interventions aimed at preventing negative developmental outcomes in children who have experienced adversity.

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          Most cited references35

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          Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults

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            Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease.

            The brain is the key organ of stress reactivity, coping, and recovery processes. Within the brain, a distributed neural circuitry determines what is threatening and thus stressful to the individual. Instrumental brain systems of this circuitry include the hippocampus, amygdala, and areas of the prefrontal cortex. Together, these systems regulate physiological and behavioral stress processes, which can be adaptive in the short-term and maladaptive in the long-term. Importantly, such stress processes arise from bidirectional patterns of communication between the brain and the autonomic, cardiovascular, and immune systems via neural and endocrine mechanisms underpinning cognition, experience, and behavior. In one respect, these bidirectional stress mechanisms are protective in that they promote short-term adaptation (allostasis). In another respect, however, these stress mechanisms can lead to a long-term dysregulation of allostasis in that they promote maladaptive wear-and-tear on the body and brain under chronically stressful conditions (allostatic load), compromising stress resiliency and health. This review focuses specifically on the links between stress-related processes embedded within the social environment and embodied within the brain, which is viewed as the central mediator and target of allostasis and allostatic load.
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              Cumulative risk and child development.

              Childhood multiple risk factor exposure exceeds the adverse developmental impacts of singular exposures. Multiple risk factor exposure may also explain why sociodemographic variables (e.g., poverty) can have adverse consequences. Most research on multiple risk factor exposure has relied upon cumulative risk (CR) as the measure of multiple risk. CR is constructed by dichotomizing each risk factor exposure (0 = no risk; 1 = risk) and then summing the dichotomous scores. Despite its widespread use in developmental psychology and elsewhere, CR has several shortcomings: Risk is designated arbitrarily; data on risk intensity are lost; and the index is additive, precluding the possibility of statistical interactions between risk factors. On the other hand, theoretically more compelling multiple risk metrics prove untenable because of low statistical power, extreme higher order interaction terms, low robustness, and collinearity among risk factors. CR multiple risk metrics are parsimonious, are statistically sensitive even with small samples, and make no assumptions about the relative strengths of multiple risk factors or their collinearity. CR also fits well with underlying theoretical models (e.g., Bronfenbrenner's, 1979, bioecological model; McEwen's, 1998, allostasis model of chronic stress; and Ellis, Figueredo, Brumbach, & Schlomer's, 2009, developmental evolutionary theory) concerning why multiple risk factor exposure is more harmful than singular risk exposure. We review the child CR literature, comparing CR to alternative multiple risk measurement models. We also discuss strengths and weaknesses of developmental CR research, offering analytic and theoretical suggestions to strengthen this growing area of scholarship. Finally, we highlight intervention and policy implications of CR and child development research and theory. © 2013 American Psychological Association
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                Author and article information

                Journal
                Current Directions in Psychological Science
                Curr Dir Psychol Sci
                SAGE Publications
                0963-7214
                1467-8721
                August 10 2016
                August 2016
                August 10 2016
                August 2016
                : 25
                : 4
                : 239-245
                Affiliations
                [1 ]Department of Psychology, University of Washington
                [2 ]Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
                Article
                10.1177/0963721416655883
                5070918
                27773969
                d9dcd1f6-1311-4bd8-8419-87bdcded60ca
                © 2016

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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