ArticleQualitative health research2018
A Discourse Analysis: One Caregiver's Voice in End-of-Life Care.
Article in Qualitative health research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Informal family caregivers make a significant contribution to the U.S. health care system, and the need for caregivers will likely increase. Gaining deeper insights into the caregiver experience will provide essential knowledge needed to support the future caregiver workforce delivering care. Discourse analysis is a viable approach in analyzing textual caregiver data that focuses on the end-of-life caregiving experience. The purpose of this study was to conduct an in-depth discourse analytic examination of 13 hours of caregiver interview data, which reveal the multiplicity of shifting stances and perceptions of one caregiver in the midst of end-of-life care, specifically with regard to his perceptions of self (caregiver) and other (care recipient). By isolating a specific but limited set of reference terms used throughout the discourse, we gained systematic glimpses into the mind and perceptions of this single caregiver in relation to his role as caregiver for his terminally ill wife.
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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.