Methods for mapping population-level interventions in system dynamics modelling for context-sensitive policy planning in youth mental health

Growing appreciation for the role of System Dynamics Modelling (SDM) in mental health decision-making is yet to translate into its widespread application. This article investigates why this is the case, aiming to identify and overcome barriers to its successful future use in supporting better decision-making.

We describe a three-stage participatory process developed iteratively across multiple regions in Australia: (1) identifying priority interventions through rapid evidence reviews and collaborating with stakeholders to understand regional needs and imminent funding decisions; (2) developing intervention-specific model parameters through the synthesis of research evidence and local contextual knowledge; and (3) using simulation scenarios as thinking tools to support critical reflection and deliberation on youth mental health investment strategies. We draw on an implementation narrative to demonstrate how this approach works in place-based regional planning practice.

The participatory processes built stakeholder capacity for systems thinking and enabled critical engagement with SDMs. Through exploring simulation scenarios, decision-makers developed appreciation for combining interventions versus implementing them separately; decision points shifted from questions of whether to fund interventions to strategic discussions about resource allocation and how to collaborate to achieve impact in the region. This methodology highlights the critical role of 'backstage work' beyond participatory workshops in which diverse forms of evidence and local contextual knowledge are translated into the formal SDM, building trust in the model while fostering capacity for model use.

System Dynamics Models can be the thinking tools that support deliberative decision-making practices. Augmenting SDMs with local expertise through these participatory practices delivers an effective method to embed these models in regional mental health decision-making, for better resource allocation and strategic planning of local mental health services.