Predictive risk modelling using administrative data is increasingly being promoted to tackle complex social policy issues, including the risk of child maltreatment and recurring involvement with child protection systems. This paper discusses opportunities and risks concerning predictive risk modelling with administrative datasets to address Indigenous Australian overrepresentation in Australian child protection systems. A scoping review using five databases, and the Google search engine, examined peer‐reviewed and grey literature on risks associated with predictive risk models (PRMs) for racial and ethnic populations in child protection systems, such as Indigenous Australians. The findings revealed a dearth of research, especially considering Indigenous populations. Although PRMs have been developed for Australian child protection systems, no empirical research was found in relation to Indigenous Australians. The implications for utilising administrative data to address Indigenous Australian overrepresentation are discussed, focusing on methodological limitations of predictive analytics, and notions of fairness and bias. Participatory model development, transparency and Indigenous data sovereignty are crucial to ensure the development of fair and unbiased PRMs in Australian child protection systems. Yet, while PRMs may offer substantial benefits as decision support tools, significant developments – which fully include Indigenous Australians – are needed before they can be used with Indigenous Australians.

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Implementation is a crucial component for the success of interventions in health service systems, as failure to implement well can have detrimental impacts on the effectiveness of evidence-based practices. Therefore, evaluations conducted in real-world contexts should consider how interventions are implemented and sustained. However, the complexity of healthcare environments poses considerable challenges to the evaluation of interventions and the impact of implementation efforts on the effectiveness of evidence-based practices. In consequence, implementation and intervention effectiveness are often assessed separately in health services research, which prevents the direct investigation of the relationships of implementation components and effectiveness of the intervention. This article describes multilevel decision juncture models based on advances in implementation research and causal inference to study implementation in health service systems. The multilevel decision juncture model is a theory-driven systems approach that integrates structural causal models with frameworks for implementation. This integration enables investigation of interventions and their implementation within a single model that considers the causal links between levels of the system. Using a hypothetical youth mental health intervention inspired by published studies from the health service research and implementation literature, we demonstrate that such theory-based systems models enable investigations of the causal pathways between the implementation outcomes as well as their links to patient outcomes. Results from Monte Carlo simulations also highlight the benefits of structural causal models for covariate selection as consistent estimation requires only the inclusion of a minimal set of covariates. Such models are applicable to real-world context using different study designs, including longitudinal analyses which facilitates the investigation of sustainment of interventions.

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