Refinement of the Shared Care Instrument-Revised: a measure of a family care interaction. J Nurs Meas 2008;16(1):43-60
Date
06/27/2008Pubmed ID
18578109DOI
10.1891/1061-3749.16.1.43Scopus ID
2-s2.0-50349102107 (requires institutional sign-in at Scopus site) 17 CitationsAbstract
This study's purpose was to evaluate the psychometric properties of the Shared Care Instrument-Revised (SCI-R) in a sample of family care dyads. The SCI-R was developed to measure the construct of shared care, which is a system of three constructs (communication, decision making, reciprocity) used in family care to exchange support. An important aspect of evaluating the SCI-R was to create a measure that is statistically sound and meaningful for patient and caregivers. Surveys were mailed to randomly selected home health dyads, which included 223 patients and 220 caregivers. Reliability and confirmatory factor analysis, and concurrent validity were examined. Internal consistency reliability of the patient subscales ranged from 0.74 to 0.76, and from 0.72 to 0.78 for caregiver subscales. Factor analysis supported the underlying theoretical basis of the SCI-R. Construct validity also was supported using the hypothesis-testing approach. One major challenge in family care research is to develop methods and tools to study the dynamic characteristics of close relationships. The findings from this study support further use of SCI-R to study how shared care facilitates the exchange of support and the influence shared care has on outcomes for both patients and caregivers.
Author List
Sebern MDAuthor
Margaret Sebern PhD Assistant Professor in the Nursing department at Marquette UniversityMESH terms used to index this publication - Major topics in bold
AgedAttitude to Health
Caregivers
Communication
Cooperative Behavior
Decision Making
Empathy
Factor Analysis, Statistical
Family
Female
Home Care Agencies
Home Nursing
Humans
Male
Middle Aged
Midwestern United States
Models, Psychological
Nursing Assessment
Nursing Evaluation Research
Nursing Methodology Research
Principal Component Analysis
Psychometrics
Social Support
Surveys and Questionnaires