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Language-Based Inequity in Health Care: Who Is the “Poor Historian”?

Overview of attention for article published in AMA Journal of Ethics, March 2017
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Mentioned by

news
3 news outlets
blogs
1 blog
twitter
60 tweeters
facebook
4 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
33 Mendeley
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Title
Language-Based Inequity in Health Care: Who Is the “Poor Historian”?
Published in
AMA Journal of Ethics, March 2017
DOI 10.1001/journalofethics.2017.19.3.medu1-1703
Pubmed ID
Abstract

Patients with limited English proficiency (LEP) are among the most vulnerable populations. They experience high rates of medical errors with worse clinical outcomes than English-proficient patients and receive lower quality of care by other metrics. However, we have yet to take the issue of linguistic inequities seriously in the medical system and in medical education, tacitly accepting that substandard care is either unavoidable or not worth the cost to address. We argue that we have a moral imperative to provide high-quality care to patients with LEP and to teach our medical trainees that such care is both expected and feasible. Ultimately, to achieve linguistic equity will require creating effective systems for medical interpretation and a major culture shift not unlike what has happened in patient safety.

Twitter Demographics

The data shown below were collected from the profiles of 60 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 18%
Researcher 4 12%
Student > Postgraduate 4 12%
Other 3 9%
Student > Bachelor 3 9%
Other 6 18%
Unknown 7 21%
Readers by discipline Count As %
Medicine and Dentistry 13 39%
Nursing and Health Professions 5 15%
Social Sciences 3 9%
Neuroscience 2 6%
Agricultural and Biological Sciences 1 3%
Other 1 3%
Unknown 8 24%