198
Views
0
CrossRef citations to date
0
Altmetric
Articles

Feasibility of Conducting Child Abuse Research with Girls in Juvenile Detention Using Audio-Computer Assisted Self-Interview (ACASI) Technology

Pages 210-235 | Published online: 21 Feb 2014
 

Abstract

This paper presents a feasibility study of audio computer-assisted self-interview (ACASI) technology used to elicit self-reported histories of child maltreatment among detained adolescent girls (N = 35). We focus on methodological, legal, ethical, and administrative challenges to the collection of sensitive information from minors who are wards of the state. We conclude that extensive collaboration with detention center staff was required to address and overcome these challenges. Pending the availability of additional evidence from demographically diverse samples, the extent to which the ACASI method improves the completeness and accuracy of child maltreatment histories reported by adolescent girls in juvenile detention remains unclear. Further research is needed to determine whether the advantages of the ACASI technology contribute to more valid and reliable reporting of maltreatment by detained girls in a setting where an empathetic interviewer, who may facilitate the disclosure process, is absent.

Acknowledgement

We are indebted to Laura Finley for her technical assistance, and to the Center Program Directors without whom this project could not have been completed.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 348.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.