JOURNAL ARTICLE

Applying the terror management theory to active shooter events: Explaining the police response.

  • Published In: International Journal of Police Science & Management, 2024, v. 26, n. 1. P. 3 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Phillips, Scott W 3 of 3

Abstract

This article examines changes in police response tactics to active shooter incidents following the 1999 Columbine shooting, highlighting the shift from securing perimeters and awaiting specialized teams to immediate engagement aimed at neutralizing shooters. It explores terror management theory (TMT), a social psychological framework positing that awareness of mortality influences behavior through cultural worldviews and self-esteem, as a means to explain why some officers may either avoid or willingly confront life-threatening situations. The article discusses how traditional police training emphasizes officer safety and survival, creating a cultural tension with public expectations that officers should risk their lives to protect others. It concludes that enhancing active shooter training to incorporate realistic exposure to mortality salience and coping mechanisms may better prepare officers to manage death anxiety and perform effectively in such high-risk events, while also calling for further research into officers' experiences and psychological responses during these incidents.

Additional Information

  • Source:International Journal of Police Science & Management. 2024/03, Vol. 26, Issue 1, p3
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:1461-3557
  • DOI:10.1177/14613557231188577
  • Accession Number:175367443
  • Copyright Statement:Copyright of International Journal of Police Science & Management is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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