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Involving members of vulnerable populations in the development of patient decision aids: a mixed methods sequential explanatory study

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#36 of 1,974)
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

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53 tweeters

Citations

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35 Dimensions

Readers on

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115 Mendeley
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Title
Involving members of vulnerable populations in the development of patient decision aids: a mixed methods sequential explanatory study
Published in
BMC Medical Informatics and Decision Making, January 2017
DOI 10.1186/s12911-016-0399-8
Pubmed ID
Authors

Michèle Dugas, Marie-Ève Trottier, Selma Chipenda Dansokho, Gratianne Vaisson, Thierry Provencher, Heather Colquhoun, Maman Joyce Dogba, Sophie Dupéré, Angela Fagerlin, Anik M. C. Giguere, Lynne Haslett, Aubri S. Hoffman, Noah M. Ivers, France Légaré, Jean Légaré, Carrie A. Levin, Matthew Menear, Jean-Sébastien Renaud, Dawn Stacey, Robert J. Volk, Holly O. Witteman

Abstract

Patient decision aids aim to present evidence relevant to a health decision in understandable ways to support patients through the process of making evidence-informed, values-congruent health decisions. It is recommended that, when developing these tools, teams involve people who may ultimately use them. However, there is little empirical evidence about how best to undertake this involvement, particularly for specific populations of users such as vulnerable populations. To describe and compare the development practices of research teams that did and did not specifically involve members of vulnerable populations in the development of patient decision aids, we conducted a secondary analysis of data from a systematic review about the development processes of patient decision aids. Then, to further explain our quantitative results, we conducted semi-structured telephone interviews with 10 teams: 6 that had specifically involved members of vulnerable populations and 4 that had not. Two independent analysts thematically coded transcribed interviews. Out of a total of 187 decision aid development projects, 30 (16%) specifically involved members of vulnerable populations. The specific involvement of members of vulnerable populations in the development process was associated with conducting informal needs assessment activities (73% vs. 40%, OR 2.96, 95% CI 1.18-7.99, P = .02) and recruiting participants through community-based organizations (40% vs. 11%, OR 3.48, 95% CI 1.23-9.83, P = .02). In interviews, all developers highlighted the importance, value and challenges of involving potential users. Interviews with developers whose projects had involved members of vulnerable populations suggested that informal needs assessment activities served to center the decision aid around users' needs, to better avoid stigma, and to ensure that the topic truly matters to the community. Partnering with community-based organizations may facilitate relationships of trust and may also provide a non-threatening and accessible location for research activities. There are a small number of key differences in the development processes for patient decision aids in which members of vulnerable populations were or were not specifically involved. Some of these practices may require additional time or resources. To address health inequities, researchers, communities and funders may need to increase awareness of these approaches and plan accordingly.

Twitter Demographics

The data shown below were collected from the profiles of 53 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 114 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 20%
Student > Master 17 15%
Student > Ph. D. Student 14 12%
Student > Bachelor 11 10%
Student > Doctoral Student 10 9%
Other 19 17%
Unknown 21 18%
Readers by discipline Count As %
Medicine and Dentistry 24 21%
Nursing and Health Professions 21 18%
Social Sciences 15 13%
Psychology 7 6%
Biochemistry, Genetics and Molecular Biology 5 4%
Other 14 12%
Unknown 29 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 05 May 2019.
All research outputs
#1,030,581
of 22,588,733 outputs
Outputs from BMC Medical Informatics and Decision Making
#36
of 1,974 outputs
Outputs of similar age
#24,454
of 397,440 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 22,588,733 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,974 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 98% 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 397,440 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% 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