Sylvie Lambert1,2, Jane McCusker3,4, Eric Belzile3, Mark Yaffe5, Chidinma Ihejirika6, Julie Richardson7, Susan Bartlett8. 1. Ingram School of Nursing, McGill University, Montreal, Canada. sylvie.lambert@mcgill.ca. 2. St. Mary's Research Centre, Montreal, Canada. sylvie.lambert@mcgill.ca. 3. St. Mary's Research Centre, Montreal, Canada. 4. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada. 5. Departments of Family Medicine, McGill University, St. Mary's Hospital Center, and the Integrated University Centre for Health and Social Services of West Island of Montreal, Montreal, Canada. 6. Ingram School of Nursing, McGill University, Montreal, Canada. 7. School of Rehabilitation, McMaster University, Hamilton, Canada. 8. Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada.
Abstract
PURPOSE: The PACIC assesses key components of the Chronic Care Model. The purpose of this study is to examine the dimensionality and psychometric properties of the PACIC. METHODS: A convenience sample of 221 adults in Canada who self-identified as living with one or more physical and/or mental chronic diseases was invited to participate via an online survey link. Rasch analysis was performed, including item and person misfit, reliability, response format, targeting, unidimensionality of subscales, and differential item functioning (DIF). Also, Confirmatory Factor Analysis (CFA) was conducted and model fit of alternative factor structures proposed for the PACIC in the literature and those suggested by the Rasch analysis were explored. RESULTS: The patient activation, delivery system, and problem-solving subscales fit the Rasch model expectations; no modifications were required. The goal setting item 10 had a disordered threshold and was recoded. Four of the five follow-up subscale items had a disordered threshold and were recoded. All subscales were unidimensional and no local dependency was detected. DIF was only detected for some items in the follow-up subscale. The CFA revealed that none of the published factor structures fit the data; the fit statistics were appropriate when item 10 was removed and the follow-up subscale was removed. CONCLUSIONS: Improving chronic disease care relies upon having validated measures to evaluate the extent to which care goals are met. With some modifications, four of the five PACIC subscales were found to be psychometrically robust.
PURPOSE: The PACIC assesses key components of the Chronic Care Model. The purpose of this study is to examine the dimensionality and psychometric properties of the PACIC. METHODS: A convenience sample of 221 adults in Canada who self-identified as living with one or more physical and/or mental chronic diseases was invited to participate via an online survey link. Rasch analysis was performed, including item and person misfit, reliability, response format, targeting, unidimensionality of subscales, and differential item functioning (DIF). Also, Confirmatory Factor Analysis (CFA) was conducted and model fit of alternative factor structures proposed for the PACIC in the literature and those suggested by the Rasch analysis were explored. RESULTS: The patient activation, delivery system, and problem-solving subscales fit the Rasch model expectations; no modifications were required. The goal setting item 10 had a disordered threshold and was recoded. Four of the five follow-up subscale items had a disordered threshold and were recoded. All subscales were unidimensional and no local dependency was detected. DIF was only detected for some items in the follow-up subscale. The CFA revealed that none of the published factor structures fit the data; the fit statistics were appropriate when item 10 was removed and the follow-up subscale was removed. CONCLUSIONS: Improving chronic disease care relies upon having validated measures to evaluate the extent to which care goals are met. With some modifications, four of the five PACIC subscales were found to be psychometrically robust.
Entities:
Keywords:
Chronic disease; Factor analysis; Health care delivery; Healthcare systems; Psychometrics; Self-management
Authors: Russell E Glasgow; Edward H Wagner; Judith Schaefer; Lisa D Mahoney; Robert J Reid; Sarah M Greene Journal: Med Care Date: 2005-05 Impact factor: 2.983
Authors: Carol Davy; Jonathan Bleasel; Hueiming Liu; Maria Tchan; Sharon Ponniah; Alex Brown Journal: BMC Health Serv Res Date: 2015-05-10 Impact factor: 2.655