Abstract
Poor long-term kidney transplant outcomes are a significant problem in the U.S. Interventions must focus on preserving allograft function by managing modifiable risk factors. An instrument capable of identifying problems with post-kidney transplant self-management behaviors may enable the design and testing of self-management interventions. This study's purpose was to test the psychometric properties of the new Kidney Transplant Self-Management Scale (KT-SM). The Zimmerman framework adapted for kidney transplant self-management guided the cross-sectional study.
Sigma Membership
Alpha
Type
Dissertation
Format Type
Text-based Document
Study Design/Type
Cross-Sectional
Research Approach
Quantitative Research
Keywords:
Kidney Transplants, Patient Activation, Self-Efficacy, Poor Long-Term Transplant Outcomes, Managing Modifiable Risk Factors, Allograft Function
Advisor
Eileen Hacker
Second Advisor
Josette Jones
Third Advisor
Susan Rawl
Fourth Advisor
Rebecca Ellis
Degree
PhD
Degree Grantor
Indiana University
Degree Year
2018
Recommended Citation
Chung, Shu-Yu, "The multidimensional kidney transplant self-management scale: Development and psychometric testing" (2022). Dissertations. 311.
https://www.sigmarepository.org/dissertations/311
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
All permission requests should be directed accordingly and not to the Sigma Repository.
All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
None: Degree-based Submission
Acquisition
Proxy-submission
Date of Issue
2022-03-24
Full Text of Presentation
wf_yes
Description
This dissertation has also been disseminated through the ProQuest Dissertations and Theses database. Dissertation/thesis number: 10929227; ProQuest document ID: 2108496045. The author still retains copyright.