SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators’ research use in public discourse Article Swipe
Taylor Scott
,
K. Megan Collier
,
Jessica Pugel
,
Patrick O’Neill
,
Elizabeth C. Long
,
Mary A. Fernandes
,
Katherine Cruz
,
Brittany Gay
,
Cagla Giray
,
D. Max Crowley
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13012-023-01268-1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13012-023-01268-1
Background While prior work has revealed conditions that foster policymakers’ use of research evidence, few studies have rigorously investigated the effectiveness of theory-based practices. Specifically, policymakers are most apt to use research evidence when it is timely, relevant, brief, and messaged appropriately, as well as when it facilitates interactive engagement. This study sought to experimentally evaluate an enhanced research dissemination intervention, known as the SciComm Optimizer for Policy Engagement (SCOPE), implemented during the COVID-19 pandemic among US state legislators. Methods State legislators assigned to health committees and their staff were randomized to receive the SCOPE intervention. This involved providing academic researchers with a pathway for translating and disseminating research relevant to current legislative priorities via fact sheets emailed directly to officials. The intervention occurred April 2020–March 2021. Research language was measured in state legislators’ social media posts. Results Legislators randomized to receive the intervention, relative to the control group, produced 24% more social media posts containing research language related to COVID-19. Secondary analyses revealed that these findings were driven by two different types of research language. Intervention officials produced 67% more COVID-related social media posts referencing technical language (e.g., statistical methods), as well as 28% more posts that referenced research-based concepts. However, they produced 31% fewer posts that referenced creating or disseminating new knowledge. Conclusions This study suggests that strategic, targeted science communication efforts may have the potential to change state legislators’ public discourse and use of evidence. Strategic science communication efforts are particularly needed in light of the role government officials have played in communicating about the pandemic to the general public.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13012-023-01268-1
- https://implementationscience.biomedcentral.com/counter/pdf/10.1186/s13012-023-01268-1
- OA Status
- gold
- Cited By
- 15
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4372332184
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4372332184Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s13012-023-01268-1Digital Object Identifier
- Title
-
SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators’ research use in public discourseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-05Full publication date if available
- Authors
-
Taylor Scott, K. Megan Collier, Jessica Pugel, Patrick O’Neill, Elizabeth C. Long, Mary A. Fernandes, Katherine Cruz, Brittany Gay, Cagla Giray, D. Max CrowleyList of authors in order
- Landing page
-
https://doi.org/10.1186/s13012-023-01268-1Publisher landing page
- PDF URL
-
https://implementationscience.biomedcentral.com/counter/pdf/10.1186/s13012-023-01268-1Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://implementationscience.biomedcentral.com/counter/pdf/10.1186/s13012-023-01268-1Direct OA link when available
- Concepts
-
Public relations, Health services research, Legislature, Social media, Public health, Intervention (counseling), Medicine, Scope (computer science), Public engagement, Health policy, Randomized controlled trial, Political science, Medical education, Nursing, Computer science, Law, Programming language, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 5, 2024: 9, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Research | 128 |
| abstract_inverted_index.academic | 100 |
| abstract_inverted_index.analyses | 163 |
| abstract_inverted_index.assigned | 83 |
| abstract_inverted_index.creating | 210 |
| abstract_inverted_index.directly | 119 |
| abstract_inverted_index.enhanced | 58 |
| abstract_inverted_index.evaluate | 56 |
| abstract_inverted_index.evidence | 33 |
| abstract_inverted_index.findings | 167 |
| abstract_inverted_index.involved | 98 |
| abstract_inverted_index.language | 129, 158, 188 |
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| abstract_inverted_index.messaged | 41 |
| abstract_inverted_index.occurred | 124 |
| abstract_inverted_index.pandemic | 75, 259 |
| abstract_inverted_index.produced | 150, 179, 204 |
| abstract_inverted_index.relative | 145 |
| abstract_inverted_index.relevant | 110 |
| abstract_inverted_index.research | 13, 32, 59, 109, 157, 175 |
| abstract_inverted_index.revealed | 6, 164 |
| abstract_inverted_index.suggests | 218 |
| abstract_inverted_index.targeted | 221 |
| abstract_inverted_index.COVID-19. | 161 |
| abstract_inverted_index.Optimizer | 66 |
| abstract_inverted_index.Secondary | 162 |
| abstract_inverted_index.Strategic | 239 |
| abstract_inverted_index.concepts. | 201 |
| abstract_inverted_index.different | 172 |
| abstract_inverted_index.discourse | 234 |
| abstract_inverted_index.evidence, | 14 |
| abstract_inverted_index.evidence. | 238 |
| abstract_inverted_index.language. | 176 |
| abstract_inverted_index.methods), | 191 |
| abstract_inverted_index.officials | 178, 252 |
| abstract_inverted_index.potential | 228 |
| abstract_inverted_index.providing | 99 |
| abstract_inverted_index.relevant, | 38 |
| abstract_inverted_index.technical | 187 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Engagement | 69 |
| abstract_inverted_index.committees | 86 |
| abstract_inverted_index.conditions | 7 |
| abstract_inverted_index.containing | 156 |
| abstract_inverted_index.government | 251 |
| abstract_inverted_index.knowledge. | 214 |
| abstract_inverted_index.officials. | 121 |
| abstract_inverted_index.practices. | 24 |
| abstract_inverted_index.priorities | 114 |
| abstract_inverted_index.randomized | 91, 140 |
| abstract_inverted_index.referenced | 199, 209 |
| abstract_inverted_index.rigorously | 18 |
| abstract_inverted_index.strategic, | 220 |
| abstract_inverted_index.Conclusions | 215 |
| abstract_inverted_index.Legislators | 139 |
| abstract_inverted_index.engagement. | 50 |
| abstract_inverted_index.facilitates | 48 |
| abstract_inverted_index.implemented | 71 |
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| abstract_inverted_index.legislative | 113 |
| abstract_inverted_index.legislators | 82 |
| abstract_inverted_index.referencing | 186 |
| abstract_inverted_index.researchers | 101 |
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| abstract_inverted_index.translating | 106 |
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| abstract_inverted_index.Intervention | 177 |
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| abstract_inverted_index.legislators. | 79 |
| abstract_inverted_index.particularly | 244 |
| abstract_inverted_index.policymakers | 26 |
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| abstract_inverted_index.Specifically, | 25 |
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| abstract_inverted_index.communication | 223, 241 |
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| abstract_inverted_index.dissemination | 60 |
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| abstract_inverted_index.intervention. | 96 |
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| abstract_inverted_index.research-based | 200 |
| abstract_inverted_index.policymakers’ | 10 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5014094194 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I130769515 |
| citation_normalized_percentile.value | 0.97393354 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |