Abstract
Background: For health organizations (private and public) to advance their care-management programs, to use resources effectively and efficiently, and to improve patient outcomes, it is germane to isolate and quantify care-management activities and to identify overarching domains.
Objectives: The aims of this study were to identify and report on an application of mixed methods of qualitative statistical techniques, based on a theoretical framework, and to construct variables for factor analysis and exploratory factor analytic steps for identifying domains of dementia care management.
Methods: Care-management activity data were extracted from the care plans of 181 pairs of individuals (with dementia and their informal caregivers) who had participated in the intervention arm of a randomized controlled trial of a dementia care-management program. Activities were organized into types, using card-sorting methods, influenced by published theoretical constructs on self-efficacy and general strain theory. These activity types were mapped in the initial data set to construct variables for exploratory factor analysis. Principal components extraction with varimax and promax rotations was used to estimate the number of factors. Cronbach's alpha was calculated for the items in each factor to assess internal consistency reliability.
Results: The two-phase card-sorting technique yielded 45 activity types out of 450 unique activities. Exploratory factor analysis produced four care-management domains (factors): behavior management, clinical strategies and caregiver support, community agency, and safety. Internal consistency reliability (Cronbach's alpha) of items for each factor ranged from.63 for the factor "safety" to.89 for the factor "behavior management" (Factor 1).
Discussion: Applying a systematic method to a large set of care-management activities can identify a parsimonious number of higher order categories of variables and factors to guide the understanding of dementia care-management processes. Further application of this methodology in outcome analyses and to other data sets is necessary to test its practicality.