This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2491) Machine Learning (1501) Clinical Information and Decision Making (1434) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (591) Electronic Health Records (1068) Adoption and Change Management of eHealth Systems (731) Validity, Reliability, and Bias (94)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40194, first published .
Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning–Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers

Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning–Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers

Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning–Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers

Authors of this article:

Aviv Y Landau1 Author Orcid Image ;   Ashley Blanchard2 Author Orcid Image ;   Nia Atkins3 Author Orcid Image ;   Stephanie Salazar4 Author Orcid Image ;   Kenrick Cato5, 6 Author Orcid Image ;   Desmond U Patton1, 7, 8 Author Orcid Image ;   Maxim Topaz9, 10 Author Orcid Image
Article Authors Cited by (1) Tweetations (7) Metrics

Books/Policy Documents

  1. Landau A, Espeleta H, Mathiyazhagan S, Blanchard A, Heider P, Cato K, Hanson R, Patton D, Lenert L, Topaz M. Handbook of Children and Screens. View