Cargando…

Chinese family care patterns of childhood rheumatic diseases: A cluster analysis

OBJECTIVES: The purpose is to distinguish family care (FC) patterns of childhood rheumatic diseases in Chinese families and to determine the predictors of FC patterns. METHODS: This secondary analysis contained two cross-section surveys with a convenient sample of totally 398 caregivers who have a c...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Jiali, Yu, Qinglin, Zhang, Taomei, Zhang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Chinese Nursing Association 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031127/
https://www.ncbi.nlm.nih.gov/pubmed/32099858
http://dx.doi.org/10.1016/j.ijnss.2019.11.005
Descripción
Sumario:OBJECTIVES: The purpose is to distinguish family care (FC) patterns of childhood rheumatic diseases in Chinese families and to determine the predictors of FC patterns. METHODS: This secondary analysis contained two cross-section surveys with a convenient sample of totally 398 caregivers who have a child with rheumatic diseases from four pediatric hospitals. Caregivers were required to completed Family Management Measure questionnaire. Cluster analysis was used to distinguish patterns and multinomial logistic regression analysis was used to find predictors. RESULTS: Four patterns were identified: the normal-perspective and collaborative (28.4%), the effortless and contradictory (24.6%), the chaotic and strenuous (18.3%), and the confident and concerning (28.7%). Disease category (χ(2) = 21.23, P = 0.002), geographic location (χ(2) = 8.41, P = 0.038), maternal educational level (χ(2) = 12.69, P = 0.048) and family monthly income (χ(2) = 33.21, P < 0.001) predicted different patterns. CONCLUSIONS: FC patterns were different among families. Disease-related and family-related factors were vital predictors to distinguish patterns consistent with the Family Management Style Framework. The result assisted that clinicians recognize FC patterns and predictors effectively to provide tailored advice in time.