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Objective Background Methods Results Conclusion This paper assesses stress disparities among marginalized parents in 2020–21 during the COVID‐19 pandemic through the mechanism of healthcare discrimination.The pandemic upended the lives of American families and had particularly stark mental health consequences for women, racial and ethnic minority (REM), and sexual and gender minority (SGM) parents. Scholars have been called to understand these unequal experiences via marginalizing mechanisms rather than using race, gender, and sexual identities as proxies for racism, sexism, and cis‐heterosexism.Structural equation modeling was used to test associations between marginalized identities and parental stress about COVID among partnered parents using healthcare discrimination, a marginalizing mechanism, as a mediator. The data come from The National Couples' Health and Time Study, a population‐representative study of couples in the United States.Findings indicate that compared to nonmarginalized parents, Black parents, women, transgender and nonbinary parents, and gay, lesbian, and bisexual parents experienced higher levels of parental stress about COVID through heightened healthcare discrimination. When accounting for healthcare discrimination, only one marginalized identity–that of women–was directly associated with parental stress about COVID along with the indirect relationship through healthcare discrimination.These findings highlight healthcare discrimination as a process that puts marginalized parents at risk for heightened stress. Parental stress has the potential to accumulate across the life course and crossover to children and communities. [ABSTRACT FROM AUTHOR]

Papers in the Special Section on Racial Disparities in Health Care stemmed from. the 60th Anniversary of Family Process Conference, The Heart of the Matter: Systemic Imperatives to Address Health Disparities and Racism in the Time of COVID, which took place in Washington, DC in September 2021. Of the 12 presenters at the conference, these four were asked to recreate their talks into articles. They address key issues that help to explain health disparities in people of color, particularly African Americans, in the United States, as well as suggest innovations to clinical interventions and health care delivery systems to better serve people who have suffered adversity from the racial inequities in the American system. [ABSTRACT FROM AUTHOR]

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward. [ABSTRACT FROM AUTHOR]