Danielle Adams

Objective To model children's mental health policy making dynamics and simulate the impacts of knowledge broker interventions. Data sources Primary data from surveys ( n = 221) and interviews ( n = 64) conducted in 2019–2021 with mental health agency (MHA) officials in state agencies. Study design 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. Data collection/extraction methods 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. Principal findings 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. Conclusions 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.