Maternal Health Experiences of Black Deaf and Hard of Hearing Women in the United States.

Introduction: Deaf and hard of hearing (DHH) women are faced with numerous health inequities, including adverse pregnancy and birth outcomes. These outcomes are likely exacerbated for Black DHH women because of the intersection of disability and race. This study aimed to explore the pregnancy and birth experiences of Black DHH women to identify factors that influence their pregnancy outcomes.

Experiences of pregnancy and perinatal healthcare access of women who are deaf: a qualitative study.

Objective: Women who are deaf experience higher rates of reproductive healthcare barriers and adverse birth outcomes compared with their peers who can hear. This study explores the pregnancy experiences of women who are deaf to better understand their barriers to and facilitators of optimal pregnancy-related health care.

Design: Qualitative study using thematic analysis.

Metrics and Algorithms for Locally Fair and Accurate Classifications using Ensembles.

To obtain accurate predictions of classifiers, model ensembles comprising multiple trained machine learning models are nowadays used. In particular, pick the most accurate model for each query object, by applying the model that performed best on similar data. Dynamic model ensembles may however suffer, similarly to single machine learning models, from bias, which can eventually lead to unfair treatment of certain groups of a general population.