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Graphic Medicine and the Limits of Biostatistics

Overview of attention for article published in AMA Journal of Ethics, September 2018
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Mentioned by

twitter
46 tweeters
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

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2 Dimensions

Readers on

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22 Mendeley
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Title
Graphic Medicine and the Limits of Biostatistics
Published in
AMA Journal of Ethics, September 2018
DOI 10.1001/amajethics.2018.897
Pubmed ID
Abstract

Increasing reliance on statistics for treatment and clinical risk assessment not only leads to the reductive interpretation of disease but also obscures ambiguities, distrust, and profound emotions that are important parts of a patient's lived experience of illness and that should be regarded as clinically and ethically relevant. Enabling critique of the limitations of statistics and illustrating their hegemonic impact on the patient's experience of illness, graphic medicine emerges as a democratic platform where marginalized perspectives on illness experiences are vindicated. Through a close reading of two carer narratives, Mom's Cancer (2006) and Janet & Me: An Illustrated Story of Love and Loss (2004), we illustrate how graphic pathographies represent experiential features of illness that are obscured by overreliance on statistical data.

Twitter Demographics

The data shown below were collected from the profiles of 46 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 27%
Professor 3 14%
Librarian 2 9%
Student > Doctoral Student 1 5%
Other 1 5%
Other 1 5%
Unknown 8 36%
Readers by discipline Count As %
Nursing and Health Professions 4 18%
Arts and Humanities 3 14%
Linguistics 1 5%
Philosophy 1 5%
Agricultural and Biological Sciences 1 5%
Other 1 5%
Unknown 11 50%