JOURNAL ARTICLE

Native Missing Persons Cases Will Not be Solved by Police Alone: The Case for "Missing Persons Advocates".

  • Published In: Journal of Interpersonal Violence, 2023, v. 38, n. 17/18. P. 10333 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gilbert, Sheena L.; Nystrom, Alyssa; Branscum, Caralin C.; Richards, Tara N.; Wright, Emily M. 3 of 3

Abstract

This study examines the potential role of victim advocacy in addressing missing person cases involving Native Americans, a population disproportionately affected by missingness due to factors such as poverty, isolation, historical trauma, and jurisdictional complexities on tribal lands. Through interviews with 25 tribal and non-tribal victim and social service providers in Nebraska, the research identifies barriers to reporting and investigating missing Native persons, including law enforcement mistrust, stereotyping, lack of communication among agencies, and limited culturally informed resources and training. Respondents highlighted the need for enhanced collaboration, increased funding, specialized training, and expanded victim service policies to better support the families of missing Native persons. The findings suggest that culturally centered victim advocacy could play a critical role in improving responses to Missing or Murdered Indigenous Persons (MMIP) cases, complementing law enforcement efforts amid systemic challenges.

Additional Information

  • Source:Journal of Interpersonal Violence. 2023/09, Vol. 38, Issue 17/18, p10333
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0886-2605
  • DOI:10.1177/08862605231171413
  • Accession Number:166099548
  • Copyright Statement:Copyright of Journal of Interpersonal Violence 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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