School violence has increased over the past ten years. This study evaluated students using a more standard and sensitive method to help identify students who are at high risk for school violence. 103 participants were recruited through Cincinnati Children’s Hospital Medical Center (CCHMC) from psychiatry outpatient clinics, the inpatient units, and the emergency department. Participants (ages 12–18) were active students in 74 traditional schools (i.e. non-online education). Collateral information was gathered from guardians before participants were evaluated. School risk evaluations were performed with each participant, and audio recordings from the evaluations were later transcribed and manually annotated. The BRACHA (School Version) and the School Safety Scale (SSS), both 14-item scales, were used. A template of open-ended questions was also used. This analysis included 103 participants who were recruited from 74 different schools. Of the 103 students evaluated, 55 were found to be moderate to high risk and 48 were found to be low risk based on the paper risk assessments including the BRACHA and SSS. Both the BRACHA and the SSS were highly correlated with risk of violence to others (Pearson correlations>0.82). There were significant differences in BRACHA and SSS total scores between low risk and high risk to others groups (p-values <0.001 under unpaired t-test). In particular, there were significant differences in individual SSS items between the two groups (p-value <0.001). Of these items, Previous Violent Behavior (Pearson Correlation = 0.80), Impulsivity (0.69), School Problems (0.64), and Negative Attitudes (0.61) were positively correlated with risk to others. The novel machine learning algorithm achieved an AUC of 91.02% when using the interview content to predict risk of school violence, and the AUC increased to 91.45% when demographic and socioeconomic data were added. Our study indicates that the BRACHA and SSS are clinically useful for assessing risk for school violence. The machine learning algorithm was highly accurate in assessing school violence risk.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Musu-Gillette, L., Zhang, A., Wang, K., Zhang, J., and Oudekerk, B.A. (2017). Indicators of school crime and safety: 2016. National Center for Education Statistics, U.S. Department of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice. Washington, DC.
Centers for Disease Control and Prevention (CDC), 1992–2014 School-Associated Violent Death Surveillance System (SAVD-SS), retrieved July 2016 from http://www.cdc.gov/injury/wisqars/index.html; and Federal Bureau of Investigation and Bureau of Justice Statistics, Supplementary Homicide Reports (SHR), preliminary data (August 2016).
National Association of School Psychologists. (2010). Crisis and safety resources. Retrieved April 3, 2014 from http://www.nasponline.org/educators/index.aspx#crisis.
McCoy DC, Roy AL, Sirkman GM. Neighborhood crime and school climate as predictors of elementary school academic quality: a cross-lagged panel analysis. Am J Community Psychol. 2013 Sep;52(1–2):128–40. https://doi.org/10.1007/s10464-013-9583-5.
Burdick-Will J. School violent crime and academic achievement in Chicago. Sociol Educ. 2013 Oct;86(4):343–61. https://doi.org/10.1177/0038040713494225.
Strøm IF, Thoresen S, Wentzel-Larsen T, Dyb G. Violence, bullying and academic achievement: a study of 15-year-old adolescents and their school environment. Child Abuse Negl 2013 Apr;37(4):243–251. doi: https://doi.org/10.1016/j.chiabu.2012.10.010. Epub 2013 Jan 6.
Gottfredson GD, Cook PJ, NA C. Schools and prevention. In: Welsh BC, Farrington DP, editors. Crime and prevention. Oxford, United Kingdom: Oxford University Press; 2000. p. 269–87.
Tanner-Smith EE, Wilson SJ, Lipsey MW. Risk factors and crime. In: Maguire M, Morgan R, Reiner R, editors. The Oxford handbook of criminology. 5th edn. Oxford: Oxford University Press; 2012. p. 89–111.
Mytton J, DiGuiseppi C, Gough D, Taylor R, Logan S. School-based secondary prevention programmes for preventing violence. Cochrane Database Syst Rev. 2006 Jul 19;3 CD004606
Park-Higgerson HK, Perumean-Chaney SE, Bartolucci AA, Grimley DM, Singh KP. The evaluation of school-based violence prevention programs: a meta-analysis. J Sch Health 2008 Sep;78(9):465–479; quiz 518-20. doi: https://doi.org/10.1111/j.1746-1561.2008.00332.x.
Borum R, Cornell DG, Modzeleski W, Jimerson SR. What can be done about school shootings? A review of the evidence. Educ Res. 2010;39(1):27–37.
Nekvasil EK, Cornell DG. Student reports of peer threats of violence: prevalence and outcomes. J Sch Violence. 2012;11(4):357–75.
Bernes KB, Bardick AD. Conducting adolescent violence risk assessments: a framework for school counselors. Prof Sch Couns. 2007;10(4):419–27.
McGowan MR, Horn RA, Mellott RN. The predictive validity of the structured assessment of violence risk in youth in secondary educational settings. Psychol Assess. 2011;23(2):478–86.
Monahan J, Steadman H. Violence risk assessment: a quarter century of research. In: Frost L, Bonnie R, editors. The evolution of mental health law. Washington: American Psychological Association; 2001. p. 195–211. https://doi.org/10.1037/10414-010.
Barzman D, Brackenbury L, Sonnier L, Schnell B, Cassedy A, Salisbury S, et al. Brief rating of aggression by children and adolescents (BRACHA): development of a tool to assess risk of inpatients’ aggressive behavior. J Am Acad Psychiatry Law. 2011;39(2):170–9.
Xia F, Yetisgen-Yildiz: Clinical corpus annotation: challenges and strategies. Proc. Of Third Workshop on Building and Evaluating Resources for Biomedical Text Mining of the International Conference on Language Resources and Evaluation, 2012.
Kors JA, Clematide S, Akhondi SA, van Mulligen EM, Rebholz-Schuhmann D. A multilingual gold-standard corpus for biomedical concept recognition: the mantra GSC. J Am Med Inform Assoc. 2015 Sep;22(5):948–56. https://doi.org/10.1093/jamia/ocv037.
Wilbur WJ, Rzhetsky A, Shatkay H. New directions in biomedical text annotation: definitions, guidelines and corpus construction. BMC Bioinformatics. 2006 Jul 25;7:356.
Barzman D, Mossman D, Sonnier L, Sorter M. Brief rating of aggression by children and adolescents (BRACHA): a reliability study. J Am Acad Psychiatry Law. 2012;40:374–82.
Barzman DH, Ni Y, Griffey M, Patel B, Warren A, Latessa E, et al. A pilot study on developing a standardized and sensitive school violence risk assessment with manual annotation. Psychiatry Q. 2017;88(3):447–57.
Douglas KS, Blanchard AJE, Guy LS, Reeves KA, Weir J (2010). HCR-20 Violence Risk Assessment Scheme: Overview and Annotated Bibliography. Retrieved from http://kdouglas.files.wordpress.com/2007/10/hcr-20-annotated-biblio-sept-2010.pdf.
Delgado SV, Barzman D, Gehle M, Caring M, Sorter MD, Kowatch R, et al. Characteristics of discharges against medical advice from acute inpatient psychiatric units for children and adolescents. Boston: Poster presented at the annual meeting of the American Academy of Child and Adolescent Psychiatry; 2007.
Hilterman EL, Nicholls TL, van Nieuwenhuizen C: Predictive performance of risk assessments in juvenile offenders: comparing the SAVRY, PCL:YV, and YLS/CMI with unstructured clinical assessments. Assessment, 2014, 21, 324, 339.
Federal Bureau of Investigation. (1999). The school shooter: a threat assessment perspective. (Federal Bureau of Investigation, ED446352). Quantico VA. Retrieved from http://www.fbi.gov/library/school/school2.pdf.
Lingren T, Deleger L, Molnar K, Zhai H, Meinzen-Derr J, Kaiser M, et al. Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements. J Am Med Inform Assoc. 2014;2013 https://doi.org/10.1136/amiajnl-2013-001837.
Deleger L, Molnar K, Savova G, Xia F, Lingren T, Li Q, et al. Large-scale evaluation of automated clinical note de-identification and its impact on information extraction. J Am Med Inform Assoc. 2012;20(1):84–94.
Ganzert S, Guttmann J, Kersting K, Kuhlen R, Putensen C, Sydow M, et al. Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning. Artif Intell Med. 2002;26(1):69–86.
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009;62(6):1609–18.
Zrimec, T., & Kononenko, I. (2004). Feasibility analysis of machine learning in medical diagnosis from aura images. In Proc. Int. Conf. KIRLIONICS-98 (Abstracts) (pp. 10–11).
Sara, NB, Halland R, Igel C, Alstrup S. High-school dropout prediction using machine learning: a Danish large-scale study European symposium on artificial neural networks, computational intelligence and machine learning. Bruges (Belgium), 22–24 April 2015.
Welsh JL, Schmidt F, McKinnon L, Chattha HK, Meyers JR A comparative study of adolescent risk assessment instruments: predictive and incremental validity assessment. 2008 Mar;15(1):104–15.
Molnar BE, Cerda M, Roberts AL, Buka SL. Effects of neighborhood resources on aggressive and delinquent behaviors among urban youths. Am J Public Health. 2008;98:1086–93. https://doi.org/10.2105/AJPH.2006.098913.
Reed MO, Jakubovski E, Johnson JA, Bloch MH. Predictor of long-term school-based behavioral outcomes in the multimodal treatment study of children with attention-deficit/hyperactivity disorder. J Child Adolesc Psychopharmacol. 27(4):296–309.
Singh JP, Grann M, Fazel S. A comparative study of violence risk assessment tools: a systematic review and metaregression analysis of 68 studies involving 25,980 participants. Clin Psychol Rev. 2011;31:499–513.
Mossman D. Assessing prediction of violence: being accurate about accuracy. J Consult Clin Psychol. 1994;62(4):783–92.
Janofsky JS, Spears S, Neubauer DN. Psychiatrists' accuracy in predicting violent behavior on an inpatient unit. Hosp Community Psychiatry. 1988;39:1090–4.
Neuman Y, Assaf D, Cohen Y, Knoll J. Profiling school shooters: automatic text-based analysis. Front Psych. 2015;6:1–5.
Shultz JM, Cohen AM, Muschert GW, Flores de Apodaca R. Fatal school shootings and the epidemiological context of firearm mortality in the United States. Disaster Health. 2013 Apr-Dec;1(2):84–101.
Flannery DJ, Modzeleski W, Kretschmar JM. Violence and school shootings. Curr Psychiatry Rep. 2013 Jan;15(1):331.
The Park Foundation, CCTST, and Cincinnati Children’s Hospital Medical Center.
Conflict of Interest
All authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
About this article
Cite this article
Barzman, D., Ni, Y., Griffey, M. et al. Automated Risk Assessment for School Violence: a Pilot Study. Psychiatr Q 89, 817–828 (2018). https://doi.org/10.1007/s11126-018-9581-8
- School violence
- School violence risk assessment
- Natural language processing
- Machine learning
- School safety
- School shootings