This chapter explores how racial bias, implicit and explicit, affects the representation and outcomes of children of color in the child welfare system. Using a national sample of 1,461 child protective services (cps) investigations in the united states, we examine differences between black and white families with regard to caseworker. As a field, we have known this for decades, yet we have been unable to effectively resolve this.
Implicit bias in the child welfare, education and mental health systems. The scale aims to improve cultural responsiveness and. Researchers have developed six main explanatory pathways for this disproportionality:
By performing a secondary data analysis on an existing dataset, this research provides new insights into how extensively c&yp are involved in research about them within. These tools are intended to increase accuracy and fairness. Research has highlighted racial and socioeconomic disparities for families in child welfare, with calls to address inequities through trainings and structural change. 1 approximately 17% of these reports were substantiated, with 618.
It suggests that implicit bias could account for the. (1) disproportionate and disparate needs of children of different racial and ethnic backgrounds; In 2020, approximately 3.9 million child abuse and neglect reports were filed to child protective services (cps) in the us. One hanging question in child welfare policy and research is whether there is an artificial overrepresentation of the poor in child welfare caseloads or whether this reflects the co.
They recognize the current concerns regarding disproportionality in child welfare services; Throughout the child welfare system. This paper reviews the literature on racial bias in the child welfare, education and mental health systems and its impact on youth of color. We summarize the causes of racial disproportionality, arguing that internal and external causes of disproportional involvement originate from a common underlying factor:.
This article reports on a focus group study that explored the factors contributing to racial disproportionality and disparity in the child welfare system. Child welfare agencies increasingly use machine learning models to predict outcomes and inform decisions. Research has highlighted racial and socioeconomic disparities for families in child welfare, with calls to address inequities through trainings and structural change. A study by txicfw researchers developed and validated a scale to assess racial and class bias among child welfare practitioners.
Research has shown that mandated reporters' decisions to report a family for child abuse or neglect are too often influenced by biases and personal.