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Objective: To determine whether the self-report frequency of inter-agency collaboration about children's mental health issues is associated with the self-report frequency of using research evidence in children's mental health policy and program decision making in mental health agencies (MHAs). Data Sources: Primary data were collected through web-based surveys of state (N = 221) and county (N = 117) MHA officials. Design: The primary independent variable was a composite score quantifying the frequency of collaboration about children's mental health issues between officials in MHAs and six other state agencies. The dependent variables were composite scores quantifying the frequency of research use in children's mental health policy and program decision making in general and for specific purposes (i.e., conceptual, instrumental, tactical, imposed). Covariates were composite scores quantifying well-established determinants of research use (e.g., agency leadership, research use skills) in agency policy and program decision making. Data Methods: Separate multiple linear regression models estimated associations between frequency of inter-agency collaboration and research use scores, adjusting for other determinants of research use, respondent state, and other covariates. Data from state and county officials were analyzed separately. Principal Findings: The frequency of inter-agency collaboration was positively and independently associated with the frequency of research use in children's mental health policy making among state ([beta] = 0.22, p = 0.004) and county ([beta] = 0.39, p < 0.0001) MHA officials. Inter-agency collaboration was also the only variable significantly associated with the frequency of research use for all four specific purposes among state MHA officials, and similar findings we observed among county MHA officials. The magnitudes of associations between inter-agency collaboration and frequency of research use were generally stronger than for more well-established determinants of research use in policy making. Conclusions: Strategies that promote collaboration between MHA officials and external agencies could increase the use of research evidence in children's mental health policy and program decision making in MHAs. KEYWORDS children, inter-agency collaboration, mental health, public policy, use of research evidence What is known on this topic * Quantitative research has identified barriers and facilitators to research use in policy making. * Qualitative research suggests that inter-agency collaboration between government officials could increase their use of research evidence in policy making, but this has not been examined using quantitative methods. What this study adds * Frequency of inter-agency collaboration is independently and positively associated with the frequency of research use among state and county mental health agency officials. * Inter-agency collaboration is more consistently and often more strongly associated with research use than well-established determinants of research use in policy making. * Inter-agency collaboration may be a promising intervention target to increase research use in mental health policy making., 1 | INTRODUCTION The United States is in the midst of a children's mental health crisis; a crisis that underscores the need for evidence-informed children's mental health policy making.1,2 Prior [...]

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. KEYWORDS health policy/politics/law/regulation, mental health, state health policies What is known on this topic * Dissemination barriers, such as lacking access to contextually relevant evidence, have been found to inhibit the use of research evidence in public agency policy making. * Prior research suggests the knowledge brokers could address these barriers. What this study adds * When knowledge brokers are present, evidence-informed policies may be more likely to be adopted. * The presence of knowledge brokers can influence consensus formation in public agencies. * The process of simulation model development and the results here serve as a prototype for agent-based models that employ social network analysis to model policy making within agencies., 1 | INTRODUCTION Policy making is a complex process. (1,2) Complex processes are characterized by behaviors that dynamically interact over time through feedback loops and evolving, nonlinear relationships to 'create [...]