Literature DB >> 30359821

Race, social class, and child abuse: Content and strength of medical professionals' stereotypes.

Cynthia J Najdowski1, Kimberly M Bernstein2.   

Abstract

Black and poor children are overrepresented at every stage of the child welfare system, from suspicion of abuse to substantiation. Focusing on stereotypes as a source of bias that leads to these disparities, the current study examines the content and strength of stereotypes relating race and social class to child abuse as viewed by medical professionals. Doctors, nurses, and other medical professionals (Study 1: N = 53; Study 2: N = 40) were recruited in local hospitals and online through snowball sampling. Study 1 identified stereotype content by asking participants to list words associated with the stereotype that either (a) Black or (b) poor children are more likely to be abused by their parents, and responses were organized into construct groups. Study 2 determined stereotype strength by asking participants to rate how strongly the constructs generated in Study 1 related to either the race-abuse or social class-abuse stereotype. The content of stereotypes linking child abuse to Black or poor children are confounded, with approximately half the constructs shared by both stereotypes. Of the 10 shared constructs, only "Stressed" and "Neglect" differed in strength, with both significantly more strongly related to the social class-abuse than race-abuse stereotype, all ts(36-37) ≤ -2.23, ps ≤ .03, Cohen's ds ≥ .71. This research documents the existence, content, and strength of stereotypes that link race and social class to child abuse. These stereotypes have the potential to lead to medical misdiagnosis of abuse for Black and poor children.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Child abuse; Diagnostic decision-making; Medical professionals; Race; Social class; Stereotype

Mesh:

Year:  2018        PMID: 30359821     DOI: 10.1016/j.chiabu.2018.10.006

Source DB:  PubMed          Journal:  Child Abuse Negl        ISSN: 0145-2134


  3 in total

1.  Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions.

Authors:  Aviv Y Landau; Susi Ferrarello; Ashley Blanchard; Kenrick Cato; Nia Atkins; Stephanie Salazar; Desmond U Patton; Maxim Topaz
Journal:  J Am Med Inform Assoc       Date:  2022-01-29       Impact factor: 4.497

2.  Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study.

Authors:  Aviv Y Landau; Ashley Blanchard; Kenrick Cato; Nia Atkins; Stephanie Salazar; Desmond U Patton; Maxim Topaz
Journal:  J Am Med Inform Assoc       Date:  2022-01-29       Impact factor: 4.497

3.  An Ecodevelopmental Framework for Engaging Diverse Youth in Foster Care and Their Families Into Technology-Based Family Intervention Research Trials.

Authors:  Johanna B Folk; Heman Gill; Catalina Ordorica; Christopher A Rodriguez; Evan D Holloway; Jocelyn Meza; Marina Tolou-Shams
Journal:  Front Digit Health       Date:  2022-05-13
  3 in total

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