Simulating the role of knowledge brokers in policy making in state agencies: An agent-based model.
health policy/politics/law/regulation
mental health
state health policies
Journal
Health services research
ISSN: 1475-6773
Titre abrégé: Health Serv Res
Pays: United States
ID NLM: 0053006
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
revised:
19
10
2021
received:
03
08
2021
accepted:
28
10
2021
pubmed:
5
3
2022
medline:
18
5
2022
entrez:
4
3
2022
Statut:
ppublish
Résumé
To model children's mental health policy making dynamics and simulate the impacts of knowledge broker interventions. Primary data from surveys (n = 221) and interviews (n = 64) conducted in 2019-2021 with mental health agency (MHA) officials in state agencies. A prototype agent-based model (ABM) was developed using the PARTE (Properties, Actions, Rules, Time, Environment) framework and informed through primary data collection. In each simulation, a policy is randomly generated (salience weights: cost, contextual alignment, and strength of evidence) and discussed among agents. Agents are MHA officials and heterogenous in their properties (policy making power and network influence) and policy preferences (based on salience weights). Knowledge broker interventions add agents to the MHA social network who primarily focus on the policy's research evidence. A sequential explanatory mixed method approach was used. Descriptive and regression analyses were used for the survey data and directed content analysis was used to code interview data. Triangulated results informed ABM development. In the ABM, policy makers with various degrees of decision influence interact in a scale-free network before and after knowledge broker interventions. Over time, each decides to support or oppose a policy proposal based on policy salience weights and their own properties and interactions. The main outcome is an agency-level decision based on policy maker support. Each intervention and baseline simulation runs 250 times across 50 timesteps. Surveys and interviews revealed that barriers to research use could be addressed by knowledge brokers. Simulations indicated that policy decision outcomes varied by policy making context within agencies. This is the first application of ABM to evidence-informed mental health policy making. Results suggest that the presence of knowledge brokers can: (1) influence consensus formation in MHAs, (2) accelerate policy decisions, and (3) increase the likelihood of evidence-informed policy adoption.
Identifiants
pubmed: 35243638
doi: 10.1111/1475-6773.13916
pmc: PMC9108216
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
122-136Subventions
Organisme : NIMH NIH HHS
ID : F31 MH122155
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK092950
Pays : United States
Organisme : NIMH NIH HHS
ID : P50 MH113662
Pays : United States
Informations de copyright
© 2022 Health Research and Educational Trust.
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