321
Views
9
CrossRef citations to date
0
Altmetric
ARTICLES

Introducing Technology in Child Welfare Referrals: A Case Study

, , , &
Pages 330-344 | Received 26 May 2015, Accepted 09 Oct 2015, Published online: 14 Dec 2015
 

Abstract

Access to social services is important for the safety of children and ultimately for reunification of families involved in the child welfare system. The process of linking families to services, however, varies by caseworker and can be cumbersome and time consuming. The Department of Children and Family Services (DCFS) Needs Portal is an Internet-based intervention to improve the timing and quality of social service referrals in Los Angeles County We used a case study approach including in-depth interviews, direct observations, and user feedback obtained from the Needs Portal to (a) determine perceived benefits and barriers to adopting the Needs Portal, and (b) report how the flow of information between users and developers was used to adapt to user needs. Our analyses revealed four major barriers: (a) caseworker apprehension regarding new technology, (b) variation in communication styles by user type, (c) lack of technological infrastructure, and (d) competing workplace demands. Information sharing between developers and users has the potential to better meet the needs of users and ultimately maximize utilization of new technology. Although Internet-based interventions are designed to inexpensively and effectively coordinate services, emerging interventions may require in-person assistance and modifications in order to succeed.

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 416.00 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.