Simulating the role of knowledge brokers in policy making in state agencies: An agent‐based model

Abstract

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.

Exploring the role of knowledge brokering in developing specialized hospitals: A descriptive qualitative study

“…Another aspect is problem solver, which includes the ability to notice, understand, and analyze problems before using their capabilities to develop the solution. This characteristic is supported by the concept of the role of KB and is included as an entrepreneurial characteristics [12,34], and it accelerates the decision on the policymaking process [28,35]. The last component of characteristics is convincing stakeholders, which is in line with the model of the approach used by a KB to transfer the ideas or knowledge to specific targets, as proven in previous studies [36][37][38].…”

“…In this study, the idealism for improvement reflects vision and selfencouragement. Moreover, theoretically, idealism implies reality, which is mind-dependent and is influenced by our ideas [28]. For the capacity-building skills, we found that the characteristic comprises several values of a KB, namely, networking skills, communication or persuasion skills, good track record, talent, broadened insight, and knowledge in their field.…”

Translating research into policy and action

“…Purtle and Colleagues 11 in this issue apply emerging systems science, implementation, and policy analysis methods to inform uptake of programs and policies at the population level. Zivin et al 12 in this issue present novel policy research focused on the health care workforce, especially when faced with potential provider burnout and labor shortages.…”

New bed configurations and discharge timing policies: A hospital‐wide simulation

“…[2][3][4][5] Discrete event simulation (DES), agent-based simulation, system dynamics and hybrid models based on these are common simulation approaches in various domains. [6][7][8][9][10][11][12][13][14][15][16] In the healthcare domain, DES is among the most commonly used method of simulation, 4,11,[17][18][19][20][21][22] where a system is modelled with a number of resources (with defined capacities), queues, and a series of events. 23 The DES approach offers better representations than other afore-mentioned individual techniques to model complex systems involving individual movements between resources and queues.…”

“…Discrete event simulation (DES), agent‐based simulation, system dynamics and hybrid models based on these are common simulation approaches in various domains 6–16 . In the healthcare domain, DES is among the most commonly used method of simulation, 4,11,17–22 where a system is modelled with a number of resources (with defined capacities), queues, and a series of events 23 .…”