JOURNAL ARTICLE

An Intersectional Analysis of Technology-Facilitated Abuse: Prevalence, Experiences and Impacts of Victimization.

  • Published In: British Journal of Criminology, 2024, v. 64, n. 3. P. 600 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Flynn, Asher; Powell, Anastasia; Hindes, Sophie 3 of 3

Abstract

This article examines technology-facilitated abuse (TFA) in Australia, presenting the first reliable national prevalence estimate based on a representative survey of 4,562 adults and 20 qualitative interviews. TFA encompasses harassing behaviours, sexual behaviours including image-based abuse, monitoring and controlling behaviours, and emotional abuse and threats conducted via digital technologies. Findings reveal that 51% of Australian adults have experienced TFA in their lifetime, with marginalized groups—such as LGB+ individuals, Indigenous people, those with disabilities, and younger adults—experiencing higher prevalence and more severe negative impacts, including harmful emotional effects and serious mental distress. The study highlights the importance of applying marginalization and intersectionality frameworks to understand how intersecting identities influence the severity of TFA's impacts, noting significant interaction effects for disability combined with language diversity and for Indigenous women. The authors emphasize the need for research, policy, and support services to address the diverse experiences of TFA victims beyond gender alone, recognizing structural inequalities that shape victimization and its consequences.

Additional Information

  • Source:British Journal of Criminology. 2024/05, Vol. 64, Issue 3, p600
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0007-0955
  • DOI:10.1093/bjc/azad044
  • Accession Number:176655658
  • Copyright Statement:Copyright of British Journal of Criminology is the property of Oxford University Press / USA 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.