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Predicting Help-Seeking Attitudes Toward Mental Health Services Among American Indian Older Adults

Predicting Help-Seeking Attitudes Toward Mental Health Services Among American Indian Older Adults American Indian (AI) older adults are vulnerable to mental health disparities, yet very little is known about the factors associated with help-seeking for mental health services among them. The purpose of this study was to investigate the utility of Andersen’s Behavioral Model in explaining AI older adults’ help-seeking attitudes toward professional mental health services. Hierarchical regression analysis was used to examine predisposing, enabling, and need variables as predictors of help-seeking attitudes toward mental health services in a sample of 233 AI older adults from the Midwest. The model was found to have limited utility in the context of older AI help-seeking attitudes, as the proportion of explained variance was low. Gender, perceived stigma, social support, and physical health were significant predictors, whereas age, perceived mental health, and health insurance were not. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Gerontology SAGE

Predicting Help-Seeking Attitudes Toward Mental Health Services Among American Indian Older Adults

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References (42)

Publisher
SAGE
Copyright
© The Author(s) 2014
ISSN
0733-4648
eISSN
1552-4523
DOI
10.1177/0733464814558875
pmid
25416511
Publisher site
See Article on Publisher Site

Abstract

American Indian (AI) older adults are vulnerable to mental health disparities, yet very little is known about the factors associated with help-seeking for mental health services among them. The purpose of this study was to investigate the utility of Andersen’s Behavioral Model in explaining AI older adults’ help-seeking attitudes toward professional mental health services. Hierarchical regression analysis was used to examine predisposing, enabling, and need variables as predictors of help-seeking attitudes toward mental health services in a sample of 233 AI older adults from the Midwest. The model was found to have limited utility in the context of older AI help-seeking attitudes, as the proportion of explained variance was low. Gender, perceived stigma, social support, and physical health were significant predictors, whereas age, perceived mental health, and health insurance were not.

Journal

Journal of Applied GerontologySAGE

Published: Jan 1, 2017

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