ArticleBMJ open quality2024
Using the communication technique of Clean Language in healthcare: an exploratory survey.
Article in BMJ open quality, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Communication is fundamental to effective healthcare. Misunderstandings can increase distress, risks and costs. Clean Language is a precision questioning technique-with specific Clean Language questions which minimise assumptions and bias. It is used in a variety of contexts but is under-researched in healthcare. An exploratory online survey was undertaken to develop an understanding of who uses Clean Language in healthcare, in what kind of roles, settings, contexts and what impacts they identify. 32 people responded, of whom 23 reported using Clean Language in a range of contexts, individually and in groups; 21 people had received training in Clean Language. Participants worked in a wide range of roles, specialties and settings, both clinical and non-clinical, and described Clean Language enabling engagement and encouragement; confidence, knowledge and power; surfacing and handling emotion and conflict in a safe way; clarity, depth, understanding and insights. Some respondents noted that Clean Language is not always intuitive and takes practice but can become natural and enjoyable to use. Communication skills training could include Clean Language questions as one way to enhance understanding. Further exploration of the impact of Clean Language, including from the perspective of patients and families, is warranted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.