Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis
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 seg...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | Implementation Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13012-018-0816-8 |
| _version_ | 1818847712480591872 |
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| author | Jonathan Purtle Félice Lê-Scherban Xi Wang Paul T. Shattuck Enola K. Proctor Ross C. Brownson |
| author_facet | Jonathan Purtle Félice Lê-Scherban Xi Wang Paul T. Shattuck Enola K. Proctor Ross C. Brownson |
| author_sort | Jonathan Purtle |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T06:05:48Z |
| format | Article |
| id | doaj.art-472b78192e9f45ffbc307532780cfc20 |
| institution | Directory Open Access Journal |
| issn | 1748-5908 |
| language | English |
| last_indexed | 2024-12-19T06:05:48Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Implementation Science |
| spelling | doaj.art-472b78192e9f45ffbc307532780cfc202022-12-21T20:33:09ZengBMCImplementation Science1748-59082018-09-0113111310.1186/s13012-018-0816-8Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysisJonathan Purtle0Félice Lê-Scherban1Xi Wang2Paul T. Shattuck3Enola K. Proctor4Ross C. Brownson5Department of Health Management & Policy, Dornsife School of Public Health, Drexel UniversityDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel UniversityDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel UniversityDepartment of Health Management & Policy, Dornsife School of Public Health, Drexel UniversityCenter for Mental Health Services Research, Brown School of Social Work, Washington University in St. LouisPrevention Research Center in St. Louis, Brown School of Social Work, Washington University in St. LouisAbstract 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.http://link.springer.com/article/10.1186/s13012-018-0816-8DisseminationAudience segmentationPolicymakerState legislatorsLatent class analysisUnited States |
| spellingShingle | Jonathan Purtle Félice Lê-Scherban Xi Wang Paul T. Shattuck Enola K. Proctor Ross C. Brownson Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis Implementation Science Dissemination Audience segmentation Policymaker State legislators Latent class analysis United States |
| title | Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis |
| title_full | Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis |
| title_fullStr | Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis |
| title_full_unstemmed | Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis |
| title_short | Audience segmentation to disseminate behavioral health evidence to legislators: an empirical clustering analysis |
| title_sort | audience segmentation to disseminate behavioral health evidence to legislators an empirical clustering analysis |
| topic | Dissemination Audience segmentation Policymaker State legislators Latent class analysis United States |
| url | http://link.springer.com/article/10.1186/s13012-018-0816-8 |
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