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Journal article
Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis
Implementation science : IS, v 13(1), pp 121-121
19 Sep 2018
PMID: 30231934
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: Elected officials (e.g., legislators) are an important but understudied population in dissemination research. Audience segmentation is essential in developing dissemination strategies that are tailored for legislators with different characteristics, but sophisticated audience segmentation analyses have not been conducted with this population. An empirical clustering audience segmentation study was conducted to (1) identify behavioral health (i.e., mental health and substance abuse) audience segments among US state legislators, (2) identify legislator characteristics that are predictive of segment membership, and (3) determine whether segment membership is predictive of support for state behavioral health parity laws.
Methods: Latent class analysis (LCA) was used. Data were from a multi-modal (post-mail, e-mail, telephone) survey of state legislators fielded in 2017 (N = 475). Nine variables were included in the LCA (e.g., perceptions of behavioral health treatment effectiveness, mental illness stigma). Binary logistic regression tested associations between legislator characteristics (e.g., political party, gender, ideology) and segment membership. Multi-level logistic regression assessed the predictive validity of segment membership on support for parity laws. A name was developed for each segment that captured its most salient features.
Results: Three audience segments were identified. Budget-oriented skeptics with stigma (47% of legislators) had the least faith in behavioral health treatment effectiveness, had the most mental illness stigma, and were most influenced by budget impact. This segment was predominantly male, Republican, and ideologically conservative. Action-oriented supporters (24%) were most likely to have introduced a behavioral health bill, most likely to identify behavioral health issues as policy priorities, and most influenced by research evidence. This was the most politically and ideologically diverse segment. Passive supporters (29%) had the greatest faith in treatment effectiveness and the least stigma, but were also least likely to have introduced a behavioral health bill. Segment membership was a stronger predictor of support for parity laws than almost all other legislator characteristics.
Conclusions: State legislators are a heterogeneous audience when it comes to behavioral health. There is a need to develop and test behavioral health evidence dissemination strategies that are tailored for legislators in different audience segments. Empirical clustering approaches to audience segmentation are a potentially valuable tool for dissemination science.
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Details
- Title
- Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis
- Creators
- Jonathan Purtle - Department of Health Management and Policy, Dornsife School of Public Health, Drexel University, Philadelphia, USAFelice Le-Scherban - Drexel UniversityXi Wang - Drexel UniversityPaul T. Shattuck - Drexel UniversityEnola K. Proctor - Washington University in St. LouisRoss C. Brownson - Washington University in St. Louis
- Publication Details
- Implementation science : IS, v 13(1), pp 121-121
- Publisher
- Springer Nature
- Number of pages
- 13
- Grant note
- R21MH111806; R25MH080916 / National Institute of Mental Health at the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) R21MH111806 / NATIONAL INSTITUTE OF MENTAL HEALTH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics; A.J. Drexel Autism Institute; Health Management and Policy; Construction Management
- Web of Science ID
- WOS:000444940100001
- Scopus ID
- 2-s2.0-85053662803
- Other Identifier
- 991019168284004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Health Policy & Services