Pregnancy and Mental Health of Young Homeless Women

Pregnancy rates among women in the U.S. who are homeless are much higher than rates among women who are housed (Greene & Ringwalt, 1998). Yet little research has addressed mental health, risk and resilience among young mothers who are homeless. This study utilizes a sample of women from the Midwest Longitudinal Study of Homeless Adolescents (MLSHA) to investigate pregnancy and motherhood over three years among unaccompanied homeless young mothers. Our data are supplemented by in-depth interviews with a subset of these women. Results show that almost half of sexually active young women (n = 222, μ age = 17.2) had been pregnant at baseline (46.4%), and among the longitudinal subsample of 171 women (μ age = 17.2), almost 70.0% had been pregnant by the end of the study. Among young mothers who are homeless, only half reported that they helped to care for their children consistently over time, and one-fifth of the women reported never seeing their children. Of the young women with children in their care at the last interview of the study (Wave 13), almost one-third met criteria for lifetime major depressive episode (MDE), lifetime posttraumatic stress disorder (PTSD), and lifetime drug abuse, and onehalf met criteria for lifetime antisocial personality disorder (APD). Twelve-month diagnoses are also reported. The impacts of homelessness on maternal and child outcomes are discussed, including the implications for practice, policy, and research

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Cross-Sector Review of Drivers and Available 3Rs Approaches for Acute Systemic Toxicity Testing

Acute systemic toxicity studies are carried out in many sectors in which synthetic chemicals are manufactured or used and are among the most criticized of all toxicology tests on both scientific and ethical grounds. A review of the drivers for acute toxicity testing within the pharmaceutical industry led to a paradigm shift whereby in vivo acute toxicity data are no longer routinely required in advance of human clinical trials. Based on this experience, the following review was undertaken to identify (1) regulatory and scientific drivers for acute toxicity testing in other industrial sectors, (2) activities aimed at replacing, reducing, or refining the use of animals, and (3) recommendations for future work in this area