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An integrated conceptual framework for evaluating and improving ‘understanding’ in informed consent

Overview of attention for article published in Trials, October 2017
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Title
An integrated conceptual framework for evaluating and improving ‘understanding’ in informed consent
Published in
Trials, October 2017
DOI 10.1186/s13063-017-2204-0
Pubmed ID
Authors

Sabine Bossert, Daniel Strech

Abstract

The development of understandable informed consent (IC) documents has proven to be one of the most important challenges in research with humans as well as in healthcare settings. Therefore, evaluating and improving understanding has been of increasing interest for empirical research on IC. However, several conceptual and practical challenges for the development of understandable IC documents remain unresolved. In this paper, we will outline and systematize some of these challenges. On the basis of our own experiences in empirical user testing of IC documents as well as the relevant literature on understanding in IC, we propose an integrated conceptual model for the development of understandable IC documents. The proposed conceptual model integrates different methods for the participatory improvement of written information, including IC, as well as quantitative methods for measuring understanding in IC. In most IC processes, understandable written information is an important prerequisite for valid IC. To improve the quality of IC documents, a conceptual model for participatory procedures of testing, revising, and retesting can be applied. However, the model presented in this paper needs further theoretical and empirical elaboration and clarification of several conceptual and practical challenges.

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The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 16%
Student > Ph. D. Student 7 14%
Student > Doctoral Student 5 10%
Researcher 5 10%
Student > Bachelor 4 8%
Other 5 10%
Unknown 15 31%
Readers by discipline Count As %
Nursing and Health Professions 9 18%
Medicine and Dentistry 7 14%
Social Sciences 3 6%
Linguistics 2 4%
Psychology 2 4%
Other 11 22%
Unknown 15 31%