The 21st century is witness to several scientific approaches to meeting pressing societal problems. Among other initiatives, the Grand Challenges for Social Work was launched by the American Academy of Social Work and Social Welfare as a call to action for the social work profession to reduce long-standing social problems through broad initiatives that are based on scientifically supported interventions. The 12 Grand Challenges for Social Work are a call to action to tackle tough social issues in the promotion of a just society, individual and family well-being, and the development of pathways to social and economic progress. In the field of child welfare, advocates are calling on the profession to reduce youth behavioral health problems by 20 percent before the end of 2025, particularly among the underprivileged and those most at risk (Hawkins et al., 2015). In support of the Grand Challenges, Children & Schools developed this special issue dedicated to examining long-standing problems in school settings that can be ameliorated through the use of scientific approaches. Working to reduce risks that impede healthy behavioral and developmental outcomes is central to any approach by school social workers to achieve the goals set forth in the Grand Challenges. School-based practitioners and researchers must enhance efforts to bridge all forms of disparities in our schools, which is key for bringing about social change. Disparities based on race or ethnicity and urbanity status, for example, perpetuate social inequities that exacerbate risk for poor development, less than optimal social outcomes, and constraints on life’s chances. Beyond the disproportionality among racial and ethnic groups, disparities may be based on other background factors such as socioeconomic level, immigration status, gender, and sexual orientation (Hawkins et al., 2015). The problems associated with social disparities are not new and have been at the forefront of a national agenda to reduce societal differences for the past 30 years. For example, the Heckler (1985) federal report on black and minority health was the first to call attention to the relationship between social issues and national inequities. Yet, despite the awareness raised, problems of disparity persist and remain important issues on the national agenda. Continued focus and vigilance by school-based practitioners in the incorporation of research findings into the practice work are instrumental in reducing the impact of long-standing risk factors that result in social disproportionality. Efforts currently underway include the 2011 National Prevention Strategy, which seeks to reduce disparities by promoting an integrated approach that focuses on wellness and prevention of poor development for all young people (U.S. Department of Health and Human Services, Surgeon General, n.d.). Individual Risk Factors for Problem Behaviors Reducing the disparity problem in schools requires foremost an understanding of the risk factors that underscore patterns of differences. A preponderance of studies have identified risk for problem behaviors, many of which have highlighted distinct differences in background factors. Among the salient student factors predicting poor development are social–emotional and social–cognitive problems such as social skills deficiencies, poor mental health, and learning problems (Bryant, Schulenberg, O’Malley, Bachman, & Johnston, 2003; Dishion & Tipsord, 2011; Jolliffe, Farrington, Loeber, & Pardini, 2016). These factors have been associated with heightened risk for behavioral problems, including aggression and substance use (Haegerich & Tolan, 2008; Henry, Tolan, Gorman-Smith, & Schoeny, 2012; Herrenkohl, Lee, & Hawkins, 2012). Findings from the Pittsburgh Youth Study reveal that low academic achievement at age 12 among African American boys and those from deprived neighborhoods is associated with greater risk for violence between the ages of 13 and 19 compared with that of white boys and those from nondeprived settings (Jolliffe et al., 2016). In addition, a growing body of work links risk for poor outcomes and exposure to trauma, such as witnessing interpersonal violence, experiences of community violence, encounters of discrimination, and natural or man-made disasters (for example, tornadoes and terrorism) (Overstreet & Chafouleas, 2016; Perfect, Turley, Carlson, Yohanna, & Gilles, 2016). It is estimated that two-thirds of all students will experience at least one traumatic event in their lifetime (Perfect et al., 2016), and there is a national movement toward creating trauma-informed school environments that support students who may be adversely affected by distressing events (Overstreet & Chafouleas, 2016). Studies also highlight distinct differences in background factors and increased risk of exposure to trauma. Data from the Project on Human Development in Chicago Neighborhoods indicate that African American youths are more likely to be exposed to violence in their homes, schools, and communities than white adolescents (Wright, Fagan, & Pinchevsky, 2013). Co-Occurring Patterns of Risk for Problem Behaviors The literature emphasizes the importance of identifying the individual factors that practitioners can focus on and that predict risk for problem behaviors. However, there is another body of work that draws attention to co-occurring patterns of risk. For example, a student with one risk factor (for example, learning problems only or social skills problems only) is likely to have a different outcome than another student with multiple co-occurring risk factors (for example, learning problems in combination with social skills problems). Likewise, a student exposed to one form of trauma (for example, sudden death of a parent) is expected to have a dissimilar outcome compared with another student who has encountered multiple traumatic events (for example, death of a parent in combination with consistent exposure to family and community violence). The number of risk factors a student is exposed to constitutes one important aspect of understanding how constellations of variables interplay to influence problem behaviors. For instance, a study by Kaplow, Curran, and Dodge (2002) observed that kindergarten and first grade children with an absence of problems such as learning difficulties, social skills deficits, and hyperactivity had less than a 10 percent chance of engaging in substance use by age 12. However, if children had two or more risk factors, the probability of substance use increases to 50 percent. Studies further demonstrate that the quantity of individual risk factors is not the only component for understanding differentiated outcomes and that the configuration of individual factors matters. Orpinas, Raczynski, Peters, Colman, and Bandalos (2014) identified patterns of risk among middle school students based on the subscales of the Behavior Assessment System for Children. In that study, the researchers identified seven patterns based on domains involving externalizing problems (aggression, hyperactivity, conduct problems), internalizing issues (anxiety, depression, somatization), school difficulties (attention and learning problems), and adaptive skills (leadership, social, and study skills). Their analyses identified a pattern of students with minimal problems in all areas at one extreme and a pattern of those with severe problems in all domains at the other extreme. There was, importantly, a range of patterns between these two extremes. For instance, identified in the analyses were students who showed a pattern of moderate problems in all areas, students with more deficits in adaptive skills while also having problems in other areas, and students with serious externalizing problems while at the same time having co-occurring school difficulties and problems in adaptive skills. All patterns reflect varying risk for aggression and differences in background factors. Among those students with severe problems in all domains, 53 percent were students of racial or ethnic minority status, and 84 percent were assessed by teachers to be at risk for aggression. In contrast, for students with few or no problems in all areas, none were assessed to be at risk for aggression but only 20 percent of this category comprised racial or ethnic minorities. Patterns of Risk and Targeted Interventions Patterns of risk, once identified, can form the basis for guiding decisions about which intervention program should target what student type, according to expected benefit. Current best practices in addressing behavioral problems focus on the whole school and a multitiered mode of service delivery. The public health framework of universal, selective, and indicated levels of intervention (Institute of Medicine, 1994), as applied to schools, is analogous to that of the educational based response-to-intervention framework (Tier 1, 2, and 3) used to assess for special education eligibility for students with disabilities (Clark & Alvarez, 2010). Based on these frameworks, school personnel are required to screen, differentiate, and match the appropriate programs not only to students already experiencing behavioral issues, but also for those at minimum, future risk for problems. Moreover, once implemented, continuous progress monitoring of students on interventions is needed to determine whether modifications and adaptations are necessary to promote program effectiveness and, ultimately, ameliorate poor outcomes. Currently, there exists a robust body of scientific evidence that has identified more than 50 targeted programs shown to reduce behavioral problems (Hawkins et al., 2015). However, there are challenges with the full implementation of best practices for school social workers in the reduction of reoccurring risk factors that impede prosocial educational outcomes. The literature reveals time and resource constraints, high caseloads, low access to research findings, competing practice demands, inconsistencies in message delivery by clinical supervisors, and policy inconsistencies as barriers to implementation of evidence-based practices (Farley et al., 2009). These factors contribute to an estimated and troubling 20-year lapse that exists in the dissemination and implementation of knowledge developed by researchers to frontline practitioners (Brekke, Ell, & Palinkas, 2007; Palinkas & Soydan, 2012). Efforts must be made to reduce this lag, and school social work researchers and practitioners must play their role. School social workers must purposely facilitate the pipeline of information needed in the translational research process. Cultural exchanges between researchers and practitioners (Palinkas & Soydan, 2012) are needed to move the field forward to decrease the disparity problem that persists in society. Opportunities to promote, collaborate, and innovate best practices between researchers and practitioners to effectively address patterns of disparities must be encouraged. The profession must look beyond individual factors and determine the patterns of co-occurring risk that can exist in any given student body. Furthermore, additional knowledge is needed to identify and then match the interventions that best reduce adverse behavioral outcomes for a particular pattern of risk. In an article published in the School Social Work: Section Connection newsletter, Raines (2010) identified the process in which evidence-based practice takes place for school social workers and cited helpful Internet resources. Conclusion Bridging the disparity gap is a formidable task requiring the collective efforts of the research and practice communities. To decrease the disparity problem that persists in society, effective targeted approaches that channel the appropriate interventions to those students at risk for poor outcomes are needed. Understanding the spectrum of risk patterns and delivery of those interventions that work best to moderate poor development are steps that practitioners and researchers should take to contribute to the national agenda on eliminating patterns of disparities. As part of their contribution to social justice and in meeting the grand challenges, school social workers must work to reduce barriers to best practices, create pathways to using evidence-based methods of intervention, and tackle risk factors that lead to disproportionality in educational opportunities and poor academic outcomes for school-age children and youths. References Brekke, J. S., Ell, K., & Palinkas, L. A. ( 2007). Translational science at the National Institute of Mental Health: Can social work take its rightful place? Research on Social Work Practice, 17, 123– 133. doi:10.1177/1049731506293693 Google Scholar CrossRef Search ADS Bryant, A. L., Schulenberg, J. E., O’Malley, P. M., Bachman, J. G., & Johnston, L. D. ( 2003). How academic achievement, attitudes, and behaviors relate to the course of substance use during adolescence: A 6-year, multiwave national longitudinal study. Journal of Research on Adolescence, 13, 361– 397. doi:10.1111/1532-7795.1303005 Google Scholar CrossRef Search ADS Clark, J. P., & Alvarez, M. ( 2010). Response to intervention: A guide for school social workers . New York: Oxford University Press. Dishion, T. J., & Tipsord, J. M. ( 2011). Peer contagion in child and adolescent social and emotional development. Annual Review of Psychology, 62, 189– 214. doi:10.1146/annurev.psych.093008.100412 Google Scholar CrossRef Search ADS Farley, A. J., Feaster, D., Schapmire, T. J., D’Ambrosio, J. G., Bruce, L. E., Oak, S., & Sar, B. K. ( 2009). The challenges of implementing evidence based practice: Ethical considerations in practice, education, policy, and research. Social Work and Society International Online Journal, 7( 2). Retrieved from http://www.socwork.net/sws/article/view/76/335 Haegerich, T. M., & Tolan, P. H. ( 2008). Core competencies and the prevention of adolescent substance use. New Directions for Child and Adolescent Development, 2008( 122), 47– 60. doi:10.1002/cd.228 Google Scholar CrossRef Search ADS Hawkins, J. D., Jenson, J. M., Catalano, R. F., Jr., Fraser, M. W., Botvin, G. J., Shapiro, V., et al. . ( 2015). Unleashing the power of prevention [Discussion paper]. Washington, DC: National Academy of Sciences. Heckler, M. ( 1985). Report of the secretary’s task force on black & minority health . Washington, DC: U.S. Department of Health and Human Services. Henry, D. B., Tolan, P. H., Gorman-Smith, D., & Schoeny, M. E. ( 2012). Risk and direct protective factors for youth violence: Results from the Centers for Disease Control and Prevention’s Multisite Violence Prevention Project. American Journal of Preventive Medicine, 43( 2, Suppl. 1), S67– S75. doi:10.1016/j.amepre.2012.04.025 Google Scholar CrossRef Search ADS Herrenkohl, T. I., Lee, J., & Hawkins, J. D. ( 2012). Risk versus direct protective factors and youth violence: Seattle Social Development Project. American Journal of Preventive Medicine, 43( 2, Suppl. 1), S41– S56. doi:10.1016/j.amepre.2012.04.030 Google Scholar CrossRef Search ADS Institute of Medicine. ( 1994). Reducing risks for mental disorders: Frontiers for preventive intervention research . Washington, DC: National Academies Press. Jolliffe, D., Farrington, D. P., Loeber, R., & Pardini, D. ( 2016). Protective factors for violence: Results from the Pittsburgh Youth Study. Journal of Criminal Justice, 45, 32– 40. doi:10.1016/j.jcrimjus.2016.02.007 Google Scholar CrossRef Search ADS Kaplow, J. B., Curran, P. J., & Dodge, K. A. ( 2002). Child, parent, and peer predictors of early-onset substance use: A multisite longitudinal study. Journal of Abnormal Child Psychology, 30, 199– 216. doi:10.1023/A:1015183927979 Google Scholar CrossRef Search ADS Orpinas, P., Raczynski, K., Peters, J. W., Colman, L., & Bandalos, D. ( 2014). Latent profile analysis of sixth graders based on teacher ratings: Association with school dropout. School Psychology Quarterly, 30, 577– 592. doi:10.1037/spq0000107 Google Scholar CrossRef Search ADS Overstreet, S., & Chafouleas, S. M. ( 2016). Trauma-informed schools: Introduction to the special issue. School Mental Health, 8( 1), 1– 6. doi:10.1007/s12310-016-9184-1 Google Scholar CrossRef Search ADS Palinkas, L. A., & Soydan, H. ( 2012). New horizons of translational research and research translation in social work. Research on Social Work Practice, 22( 1), 85– 92. doi:10.1177/1049731511408738 Google Scholar CrossRef Search ADS Perfect, M. M., Turley, M. R., Carlson, J. S., Yohanna, J., & Gilles, M.P.S. ( 2016). School-related outcomes of traumatic event exposure and traumatic stress symptoms in students: A systematic review of research from 1990 to 2015. School Mental Health, 8( 1), 7– 43. doi:10.1007/s12310-016-9175-2 Google Scholar CrossRef Search ADS Raines, J. ( 2010). Evidence-based practice in school social work: Clarifying concepts and common confusions. In School Social Work: Section Connection (Issue 1, pp. 9–10). Washington, DC: National Association of Social Workers. Available at https://www.socialworkers.org/assets/secured/documents/sections/school/newsletters/2010%20School%20Social%20Work%20Newsletter%20-%20Issue%201.pdf U.S. Department of Health and Human Services, Surgeon General. (n.d.). National Prevention Strategy. Retrieved from http://www.surgeongeneral.gov/priorities/prevention/strategy/ Wright, E. M., Fagan, A. A., & Pinchevsky, G. M. ( 2013). The effects of exposure to violence and victimization across life domains on adolescent substance use. Child Abuse & Neglect, 37, 899– 909. doi:10.1016/j.chiabu.2013.04.010 Google Scholar CrossRef Search ADS © 2017 National Association of Social Workers
Children & Schools – Oxford University Press
Published: Jan 1, 2018
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