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Conducting Research with Stigmatized Populations: Practices, Challenges, and Lessons Learned

Maestre, JF; Eikey, EV; Warner, M; Yarosh, S; Pater, J; Jacobs, M; Marcu, G; (2018) Conducting Research with Stigmatized Populations: Practices, Challenges, and Lessons Learned. In: Proceeding of the CSCW '18: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. (pp. pp. 385-392). ACM: Jersey City, NJ, USA. Green open access

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Abstract

Conducting research with communities who are at risk of being stigmatized can be a challenging endeavor. It is often difficult to reach and recruit individuals for research purposes regarding a stigmatized condition or situation. Yet, researchers in our field have recognized the importance of work in this area and have individually developed a range of strategies to reach, recruit, and work with these populations. This workshop will invite researchers and practitioners to present, discuss, and compare strategies and experiences when working with stigmatized communities in the context of the ever-evolving nature of technology. The outcomes of the workshop will include an outline for an article that will summarize the strategies and practices discussed as well as identify the approaches that have led to the best outcomes across different populations.

Type: Proceedings paper
Title: Conducting Research with Stigmatized Populations: Practices, Challenges, and Lessons Learned
Event: CSCW '18: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing
Location: Jersey City, NJ, USA
Dates: 03 November 2018 - 07 November 2018
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3272973.3273003
Publisher version: https://doi.org/10.1145/3272973.3273003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: stigma, stigmatized populations, research with stigmatized populations
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10062383
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