Social Work - Research Publications
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ItemNo Preview AvailableHuman capital and child protection: A research framework in the CRC context(PERGAMON-ELSEVIER SCIENCE LTD, 2021-09)Since it was adopted November 1989, the Convention on the Rights of the Child has shaped the objectives for child protection systems around the world. Generally, those objectives fall along three dimensions: permanency, safety, and well-being. However, despite well-being receiving increasing attention in light of evidence that points to the importance of early childhood experiences on life course outcomes, child protection systems have so far struggled to find clear definition of well-being as a developmental construct. In this article, we propose a definition of child well-being that draws on the economic literature pertaining to skill formation and human capital. We argue that human capital, as a multidimensional concept that incorporates cognitive skills, non-cognitive skills, and health, should be added to the list of considerations policy makers contemplate when their attention turns to well-being provided there is research evidence for doing so. To that end, we discuss the several advantages the human capital framework offers within a child protection context. We then describe a theoretical framework and analytical approach to the study of skill formation. We are particularly interested in dynamic models wherein the skills one has influence the rate at which new skills are acquired, with specific emphasis on risk and protective factors across the life course of childhood. Overall, our discussion highlights how a dynamic model of human capital formation aligns with Convention on the Rights of the Child and notions that children in child protection systems have a right to develop the abilities they will need to be responsible adults.
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ItemThe engagement of children in out-of-home care with nursing and allied health professionals: A scoping review(WILEY, 2021-11)BACKGROUND: Children living in out-of-home care (OOHC) have significant unmet health care needs and use more tertiary and specialist health care services compared with children from similar social and economic backgrounds. Allied health professionals and nurses have a central role in health care; however, very little is known about the engagement of children in OOHC with nursing and allied health professionals. This scoping review addresses this knowledge gap. METHODS: A scoping review methodology framework was used to search for relevant articles published between January 1970 and November 2019, identified using three databases: MEDLINE, CINAHL and ProQuest. Selection of studies was based on empirical research about the health of children in OOHC and their engagement with nursing or allied health services. A total of 37 relevant articles met the eligibility criteria for inclusion in this review. RESULTS: Findings could be summarized under five broad themes: (1) nursing and allied health professionals engaging with children in OOHC to support their health and development, (2) opportunities and challenges for nursing and allied health professionals to engage children in OOHC in healthcare, (3) identification and complexity of healthcare needs, (4) access to healthcare services and (5) coordination of healthcare. CONCLUSIONS: Children in OOHC have multiple healthcare needs that require monitoring and treatment by allied health professionals and the health and development of these children is best supported through comprehensive health screening on entry into OOHC, and community-based, multidisciplinary healthcare while children are living in OOHC. While nurses in hospitals and community settings were found to play a role in health assessment and care coordination targeted at children in OOHC, the literature was silent on the role of allied health professionals in this healthcare approach.
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ItemNo Preview AvailableWho is analysing what? The opportunities, risks and implications of using predictive risk modelling with Indigenous Australians in child protection: A scoping review(WILEY, 2021-06)Abstract 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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ItemData on children reentering foster care from kinship guardianship(ELSEVIER SCIENCE BV, 2018-06)This article describes a dataset containing information on children exiting to kinship guardianship in California between 2003 and 2010 (N = 18,831). Children and young people in the sample were followed for up to fourteen years. The data presented here show summary statistics of the sample included in the analysis. Furthermore, the data consist of life tables showing counts of children at risk of reentry, counts of children who reentered the foster care system as well as nonparametric estimates of the survival function and the cumulative hazard function for the period 2003-2017.
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ItemDecision-based models of the implementation of interventions in systems of healthcare: Implementation outcomes and intervention effectiveness in complex service environments(PUBLIC LIBRARY SCIENCE, 2019-10-17)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.