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Psychiatric Quarterly

, Volume 89, Issue 4, pp 817–828 | Cite as

Automated Risk Assessment for School Violence: a Pilot Study

  • Drew BarzmanEmail author
  • Yizhao Ni
  • Marcus Griffey
  • Alycia Bachtel
  • Kenneth Lin
  • Hannah Jackson
  • Michael Sorter
  • Melissa DelBello
Original Paper

Abstract

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.

Keywords

School violence School violence risk assessment Natural language processing Machine learning School safety School shootings 

Notes

Acknowledgments

The Park Foundation, CCTST, and Cincinnati Children’s Hospital Medical Center.

Compliance with Ethical Standards

Conflict of Interest

All authors declare that they have no conflict of interest.

Ethical Approval

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

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Drew Barzman
    • 1
    Email author
  • Yizhao Ni
    • 1
  • Marcus Griffey
    • 1
  • Alycia Bachtel
    • 1
  • Kenneth Lin
    • 1
  • Hannah Jackson
    • 2
  • Michael Sorter
    • 1
  • Melissa DelBello
    • 3
  1. 1.Cincinnati Children’s Hospital Medical Center (CCHMC)CincinnatiUSA
  2. 2.University of DaytonDaytonUSA
  3. 3.University of CincinnatiCincinnatiUSA

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