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Expanding Ethics Review Processes to Include Community-Level Protections: A Case Study from Flint, Michigan

Overview of attention for article published in AMA Journal of Ethics, October 2017
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3 tweeters

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Title
Expanding Ethics Review Processes to Include Community-Level Protections: A Case Study from Flint, Michigan
Published in
AMA Journal of Ethics, October 2017
DOI 10.1001/journalofethics.2017.19.10.ecas3-1710
Pubmed ID
Abstract

As the Flint community endeavors to recover and move forward in the aftermath of the Flint water crisis, distrust of scientific and governmental authorities must be overcome. Future community engagement in research will require community-level protections ensuring that no further harm is done to the community. A community ethics review explores risks and benefits and complements institutional review board (IRB) review. Using the case of Flint, I describe how community-level ethical protections can reestablish a community's trust. All IRBs reviewing protocols that include risk to communities and not merely individual participants should consider how community members are engaged in the proposed research and identify and respond to questions and domains of concern from community members.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 21%
Student > Master 5 21%
Student > Doctoral Student 3 13%
Student > Ph. D. Student 2 8%
Lecturer 1 4%
Other 0 0%
Unknown 8 33%
Readers by discipline Count As %
Medicine and Dentistry 6 25%
Nursing and Health Professions 4 17%
Environmental Science 2 8%
Social Sciences 2 8%
Business, Management and Accounting 1 4%
Other 1 4%
Unknown 8 33%