Buy this Article for $10.95

Have a coupon or promotional code? Enter it here:

When you buy this you'll get access to the ePub version, a downloadable PDF, and the ability to print the full article.


caregivers, caregiving effectiveness, home technology care



  1. Smith, Carol E.


Background: Original testing of the Caregiving Effectiveness Model, in a randomly drawn national sample (n = 111) of family caregivers, explained variance in the home care outcomes of patient physical condition, technology side effects, and quality of life. The variables in the resulting model reflected the challenges specific to family caregivers managing complex home care for the growing populations of technology-dependent patients.


Objective: To seek further empirical verification of the relationships among home care outcomes and the variables in the original trimmed model.


Method: Data were collected from family caregivers (n = 31) and adult patients (n = 31) requiring lifelong daily total parenteral nutrition (TPN) infusion technology for nonmalignant bowel disease. Hierarchical regression was used with variables entered in the two stages that coincided with the model configuration of Caregiving and Adaptive concepts, with a criteria of alpha = .05 at a power of >.80.


Results: The model variables explained variance in all four outcomes. Specifically, Caregiving and Adaptive concept variables contributed to the explained variance in quality of life of both caregivers (R2 = .559, F = 4.65, p = .003) and patients (R2 = .464, F = 5.17, p = .04). Variance in patients' physical condition (R2 = .345, F = 6.37, p = .032) and the technological side effects outcomes (R2 = .357, F = 3.60, p = .018) were accounted for by variables in the model.


Conclusions: In this sample, the Caregiving Effectiveness Model concepts accounted for significant variance in home care outcomes (quality in patients' and caregivers' lives, patients' physical condition, and technological side effect). Longitudinal study of this sample will determine if variables explain variance over time, as in the original model testing.