Abstract:Administrative data are data regularly collected by organizations for monitoring and documentation purposes. They usually represent entire populations; they are timely; and have direct influence on their sources which are mostly governmental agencies. We argue in this paper that administrative data can and should be used as indicators of children's well-being as they constitute an existing body of knowledge that has the potential to form and influence policy. Such use of administrative data as of child well-be… Show more
“…IDS developed in partnership support rapid knowledge-to-practice development cycles, providing insights about how policy or programme changes in one area affect a group's outcomes in another. They address gaps in resources and provide a basis for improving social policies and creating new services [30][31][32][33]. Cyclical programme and policy evaluation using IDS help ensure continuous impact assessment and quality improvement.…”
Section: Relevant Indicators Collected By Idsmentioning
The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.
“…IDS developed in partnership support rapid knowledge-to-practice development cycles, providing insights about how policy or programme changes in one area affect a group's outcomes in another. They address gaps in resources and provide a basis for improving social policies and creating new services [30][31][32][33]. Cyclical programme and policy evaluation using IDS help ensure continuous impact assessment and quality improvement.…”
Section: Relevant Indicators Collected By Idsmentioning
The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.
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