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The design of a low literacy decision aid about rheumatoid arthritis medications developed in three languages for use during the clinical encounter

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2014
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  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

twitter
7 tweeters

Citations

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28 Dimensions

Readers on

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58 Mendeley
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Title
The design of a low literacy decision aid about rheumatoid arthritis medications developed in three languages for use during the clinical encounter
Published in
BMC Medical Informatics and Decision Making, November 2014
DOI 10.1186/s12911-014-0104-8
Pubmed ID
Authors

Jennifer L Barton, Christopher J Koenig, Gina Evans-Young, Laura Trupin, Jennie Anderson, Dana Ragouzeos, Maggie Breslin, Timothy Morse, Dean Schillinger, Victor M Montori, Edward H Yelin

Abstract

BackgroundShared decision-making in rheumatoid arthritis (RA) care is a priority among policy makers, clinicians and patients both nationally and internationally. Demands on patients to have basic knowledge of RA, treatment options, and details of risk and benefit when making medication decisions with clinicians can be overwhelming, especially for those with limited literacy or limited English language proficiency. The objective of this study is to describe the development of a medication choice decision aid for patients with rheumatoid arthritis (RA) in three languages using low literacy principles.MethodsBased on the development of a diabetes decision aid, the RA decision aid (RA Choice) was developed through a collaborative process involving patients, clinicians, designers, decision-aid and health literacy experts. A combination of evidence synthesis and direct observation of clinician-patient interactions generated content and guided an iterative process of prototype development.ResultsThree iterations of RA Choice were developed and field-tested before completion. The final tool organized data using icons and plain language for 12 RA medications across 5 issues: frequency of administration, time to onset, cost, side effects, and special considerations. The tool successfully created a conversation between clinician and patient, and garnered high acceptability from clinicians.ConclusionsThe process of collaboratively developing an RA decision aid designed to promote shared decision making resulted in a graphically-enhanced, low literacy tool. The use of RA Choice in the clinical encounter has the potential to enhance communication for RA patients, including those with limited health literacy and limited English language proficiency.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Student > Master 9 16%
Researcher 8 14%
Student > Doctoral Student 4 7%
Professor 4 7%
Other 14 24%
Unknown 8 14%
Readers by discipline Count As %
Medicine and Dentistry 21 36%
Psychology 6 10%
Social Sciences 6 10%
Nursing and Health Professions 6 10%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Other 6 10%
Unknown 10 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2016.
All research outputs
#5,140,335
of 16,652,557 outputs
Outputs from BMC Medical Informatics and Decision Making
#580
of 1,521 outputs
Outputs of similar age
#84,024
of 292,700 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 16,652,557 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,521 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 292,700 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them