Designing mental health promotion campaigns: segmenting U.S. Veteran audiences to address public stigma
Public stigma is a significant deterrent to mental health service use for U.S. veterans. Media campaigns are often used to dispel stigmatizing beliefs and actions. Segmentation is an evidence-based practice for their effective use; however, little data has been published on veteran segments to targe...
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Published in | Journal of mental health (Abingdon, England) Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 7 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
England
Routledge
01.06.2024
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | Public stigma is a significant deterrent to mental health service use for U.S. veterans. Media campaigns are often used to dispel stigmatizing beliefs and actions. Segmentation is an evidence-based practice for their effective use; however, little data has been published on veteran segments to target with anti-stigma messages.
This article aims to identify and describe initial typologies of stigmatizing attitudes within a group of U.S. military veterans.
Telephone-based cross-sectional surveys were conducted with a national random sample of veterans from 2014 to 2016 (N = 2142). Stigma outcomes were measured using a brief, validated instrument used in population-based surveys of public perceptions toward people with mental illness. Cluster analysis was conducted to identify specific groupings along multiple dimensions.
A final four-cluster solution was identified among veterans with distinct patterns of attitudes toward mental illness and include: 1) the undecided, 2) the influencer, 3) the ambivalent, and 4) the potential ally. Several strategies were also identified for designing anti-stigma messaging toward these segments.
This research demonstrates veterans can be segmented by attitudes to target with anti-stigma campaign messages. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0963-8237 1360-0567 1360-0567 |
DOI: | 10.1080/09638237.2022.2069712 |