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Improving victim engagement and officer response in rape investigations: A longitudinal assessment of a brief training

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  • Mourtgos, Scott M.
  • Adams, Ian T.
  • Mastracci, Sharon H.

Abstract

The initial interaction between rape victims and police officers affects how cases progress through the criminal justice system. In one US state capitol, the police agency determined its initial response to rape victims was sub-par. Victim engagement was low, and officer-written reports often endorsed negative stereotypes about rape victims. A four-hour training to enhance police response was developed and implemented. Within four months, all sworn officers (n ~ 600) completed an in-person, four-hour training. We first test the effects of training on the percentage of rape victims who stay engaged in the investigative process following their initial contact with officers. We then use a machine-learning-based text analysis of all written reports (77 pre-training, and 55 post-training cases) of initial contacts between officers and victims. Compared to the six months before training, victim engagement improved 32% in the post-training period. Written reports by officers also improved, with increased victim-supportive language and improved focus on victim services.

Suggested Citation

  • Mourtgos, Scott M. & Adams, Ian T. & Mastracci, Sharon H., 2021. "Improving victim engagement and officer response in rape investigations: A longitudinal assessment of a brief training," Journal of Criminal Justice, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:jcjust:v:74:y:2021:i:c:s0047235221000386
    DOI: 10.1016/j.jcrimjus.2021.101818
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    References listed on IDEAS

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    1. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    2. Bouffard, Jeffrey A., 2000. "Predicting type of sexual assault case closure from victim, suspect, and case characteristics," Journal of Criminal Justice, Elsevier, vol. 28(6), pages 527-542.
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    Cited by:

    1. Campbell, Bradley A. & Wells, William & King, William R., 2021. "What happens when sexual assault kits go untested? A focal concerns analysis of suspect identification and police pre-arrest decisions," Journal of Criminal Justice, Elsevier, vol. 76(C).
    2. Lovell, Rachel E. & Klingenstein, Joanna & Du, Jiaxin & Overman, Laura & Sabo, Danielle & Ye, Xinyue & Flannery, Daniel J., 2023. "Using machine learning to assess rape reports: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes," Journal of Criminal Justice, Elsevier, vol. 88(C).

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