Year of Award

2009

Document Type

Thesis

Degree Type

Master of Arts (MA)

Degree Name

Clinical Psychology

Department or School/College

Department of Psychology

Committee Chair

Gyda Swaney, PhD

Commitee Members

David Schuldberg, PhD, Annie Belcourt, PhD

Keywords

American Indians, Trauma, Aging, Health Issues, Resilience

Subject Categories

Geropsychology | Health Psychology | Multicultural Psychology | Psychology

Abstract

The average life expectancy of American Indian (AI) older adults has paralleled mainstream aging trends and is set to continue growing as global increases in longevity continue to improve (Jervis, Boland, & Fickenscher, 2010). However, the disproportionately high levels of chronic health conditions (e.g., diabetes, hypertension, cerebrovascular diseases) observed in this group may outstrip the coping resources of some individuals, potentially leading to unsuccessful aging outcomes such as adverse mental health outcomes (specifically depression). As described in Goins and Pilkerton (2010, p. 346), comparatively higher rates of chronic health conditions have created an “expansion of morbidity,” where American Indians are developing chronic diseases earlier and living with them for longer periods of time. In the present study, secondary analyses were conducted with 158 AI older adults and elderly (aged 50 years or older) to determine how demographic variables, physical health factors, and personal coping resources influence the development of depression symptoms as measured by the Center of Epidemiological Studies Depression Scale (CES-D). A multiple hierarchical linear regression with nine predictors was used to examine CES-D scores as a continuous variable. The overall three-step linear model accounted for significant variance in total CES-D scores [R2 = .485, R2 change = .106, p < .001], with education status, number of reported chronic health conditions, self-reported health status, perceived social support, and personal mastery emerging as significant predictors. A multiple hierarchical logistic regression was also conducted to assess the model’s ability to differentiate asymptomatic (i.e., CES-D ≤ 15) from symptomatic (i.e., CES-D ≥ 16) depression subgroups. The three-step logistic model added statistically significant improvement over the constant-only model [χ2 (9, N = 157) = 62.671, p < .001]. In the full three-step logistic model, only chronic health conditions and personal mastery were found to differentiate the two depression subgroups. These findings are discussed in the context of enhancing resiliency against depression in late life.

Download

Share

COinS
 

© Copyright 2009 Ennis F. Vaile