Racial Bias Research Articles

Getting Help During Active Pain Crises in Sickle Cell Disease: Patient and Caregiver Perspectives in Canada

Plain Language Summary What is this summary about? This is a plain language summary of an article originally published in Patient Preference and Adherence. This study aimed to identify the notable symptoms and impacts of sickle cell disease from the point of view of individuals living with sickle cell disease and their caregivers. The study also sought to understand the factors involved in individuals living with sickle cell disease’ and caregivers’ decisions and preferences if and when they seek care during a pain crisis. This summary describes: the symptoms experienced by adolescents and adults living with sickle cell disease and their severity; the treatments they and their caregivers prefer to use when they are experiencing a pain crisis; and the factors that go into deciding when and whether individuals living with sickle cell disease and their caregivers should seek outside help -- to go to a hospital, for example - during a pain crisis. What were the results? Individuals living with sickle cell disease undergoing an acute pain crisis and their caregivers consider many factors when deciding whether to seek care at a medical facility. These include: The intensity of their symptoms If the facility has a treatment plan for them already in place If the facility has long wait times If the facility has an understanding and compassionate staff The individual's age and ability to manage their daily responsibilities Racial bias, shown in the actions and lack of empathy of Emergency Department staff toward individuals living with sickle cell disease, frequently factored into the individuals' and caregivers’ decision-making and hinders their ability to get the treatment needed during a pain crisis. What do the results mean? There is a need for a unified care team at medical facilities that treats all individuals living with sickle cell disease with the same high level of empathy and treatment quality and for a strong support system for individuals living with sickle cell disease outside of the hospital or clinic. This is an abstract of the Plain Language Summary of Publication article. View the full Plain Language Summary PDF of this article to read the full-text Link to original article here

‘ALERT’, a machine learning predictive model to identify six-month mortality in patients with advanced cancer: Perception of the utility and acceptability among patients.

189 Background: Machine learning (ML) models can predict mortality and guide end-of-life (EOL) care for patients with advanced solid cancers. Early advanced care planning (ACP) leads to more goal-concordant care, yet many patients, especially from minoritized groups, do not engage in serious illness conversations (SIC). Reasons for delay include inability of oncologists to identify patients at high short-term mortality risk, and uncertainties about prognosis. While ML may improve prognosis accuracy, research on its use and impact on racial biases affecting SIC is limited. This study aims to evaluate the acceptability of deploying a ML model to predict 6-month mortality among patients with advanced solid cancers. Methods: In order to inform the implementation of ‘ALERT,’ a predictive mortality model into a clinical alert system, we conducted one-on-one semi-structured interviews with patients with advanced solid cancer receiving care at a single institution (N=26). Interviews were recorded, transcribed verbatim, coded, and themes were reviewed by a team of interdisciplinary investigators. Results: Benefits and challenges to guide the future implementation of ALERT were identified. Patient-perceived benefits were: 1) EOL Planning: preparing family members (e.g., financial, emotional); 2) Emotional Freedom: knowledge would give freedom to live out life more carefree due to limited time left; 3) Medical Autonomy: the treatment could be worse than the actual disease, if at any point it was not helping, knowing allows individuals to make the decision to stop. Challenges identified were: 1) Loss of Control: “when you don’t know [the outcome], you just keep moving, you just keep pushing”- knowing can result in a loss of control patients may desire for their course of treatment 2) Emotional Impact: knowledge of mortality could lead to grief among patients and families leading to distress around acceptability; 3) Faith: mortality prediction can be seen “playing God,” many patients believed when their time comes, it will happen and a ML tool cannot accurately predict when that time will be. Conclusions: Patients expressed equal balance of acceptability and resistance of ALERT for themselves and their families. This resulted in the difference of preferences regarding whether or not the knowledge this tool could provide would be helpful. This could lead to clinicians taking a patient-centered approach where the delivery of results using ALERT can be based on patient preference. The findings underscore the complexity of implementing ALERT, balancing significant benefits with notable challenges. These insights will guide the refinement of ALERT, ensuring it supports patient care while ensuring ALERT’s benefits are maximized and challenges are overcome.

Optimizing the fairness of survival prediction models for racial/ethnic subgroups: A study on predicting post-operative survival in stage IA and IB non-small cell lung cancer.

380 Background: The recent surge of utilizing machine learning (ML) to develop prediction models for clinical decision-making aids is promising. However, these models can demonstrate racial bias due to inequities in real-world training data. In lung cancer, multiple models have been developed to predict prognosis, but none have been optimized to mitigate bias in performance among racial/ethnic subgroups. We developed a ML model to predict five-year survival in Stage 1A-1B non-small cell lung cancer (NSCLC), ensuring fairness on race. Methods: In the National Cancer Database, we identified patients with histopathologically confirmed stage 1A -1B NSCLC who underwent curative intent lobectomy from 2004 – 2017. We split the study cohort into a training and test sets (70%/30%). We trained and compared various ML models to predict 5-year overall survival. Patient demographic, clinical, and disease characteristics were used as input features for the models. To evaluate model fairness, we used the equalized odds ratio (eOR), which compares the true positive and false positive rates across groups; an eOR value of 1 represents equivalent rates across racial groups. We utilized 3 approaches to mitigate model bias and optimize for fairness of the best “naïve” model: grid search, threshold optimizer, and the exponentiated gradient methods. We evaluated model performance before and after bias mitigation using the area under the curve (AUC). Results: 124,298 patients fit our inclusion/exclusion criteria; 87% of patients were White, 8% were Black/African American, 3% Hispanic, and 2% Asian. Eighty percent of patients were diagnosed with stage 1A cancer; 20% had stage 1B cancer. The best naïve ML model, not optimized for fairness on race, had an eOR of 0.25 with an AUC of 0.66 (95% CI 0.65-0.66) overall. This model demonstrated an AUC of 0.65 (0.65-0.66) among white patients, 0.64 (0.62-0.66) among Black patients, 0.64 (0.60-0.68) among Asian patients, and 0.71 (0.68-0.74) among Hispanic patients. The threshold optimizer bias mitigation strategy improved fairness the most while maintaining similar overall performance of AUC 0.65 (0.64-0.66). With this strategy the eOR improved to 0.83 while AUC remained relatively stable across racial subgroups. Conclusions: We developed a ML model to predict 5-year survival in patients undergoing surgery for stage IA-IB NSCLC and employed model bias mitigation strategies that significantly improved model fairness, without diminishing overall performance. These strategies should be considered when developing prediction models for clinical decision making to avoid perpetuating disparities in care due to algorithm bias. Model performance metrics. Equalized Odds Ratio True Positive Rate False Positive Rate AUC Naive model 0.25 0.61 0.30 0.66 Threshold optimizer mitigated model 0.83 0.62 0.32 0.65

PROMOVENDO ACESSO EQUITATIVO À EDUCAÇÃO PARA COMUNIDADES MARGINALIZADAS E GRUPOS VULNERÁVEIS

Equitable access to education is a global challenge that affects many marginalized communities and vulnerable groups, who often face significant barriers. This article explores the main obstacles encountered by these communities, such as economic barriers, structural inequalities, discrimination, and geographic limitations. Economic barriers include the lack of financial resources to cover educational costs, while structural inequalities reflect disparities in access to quality infrastructure and services. Discrimination can manifest in various forms, such as racial, ethnic, or gender bias, and geographic access issues refer to physical distance and inadequate infrastructure in remote areas. To promote truly equitable access, it is crucial to adopt strategies such as implementing inclusive educational policies, offering scholarship programs, and developing diversity-sensitive education. Additionally, community interventions and the use of educational technologies can play vital roles in overcoming these barriers. These strategies aim to ensure that all individuals, regardless of their socioeconomic background, ethnicity, or location, could access quality education. By addressing these challenges collaboratively and implementing effective measures, we can move towards a more just and inclusive society, where education is genuinely accessible to everyone.

Racial Supremacy Bias Witnessed in Robert Henry Wallace Dunlop’s Service and Adventure with the Khakee Ressalah; or, Meerut Volunteer Horse during the Mutinies of 1857-58 : A Historical Survey

Racial Supremacy Bias Witnessed in Robert Henry Wallace Dunlop’s Service and Adventure with the Khakee Ressalah; or, Meerut Volunteer Horse during the Mutinies of 1857-58 : A Historical Survey

Linguistic analysis of letters of recommendation for pediatric otolaryngology: A pilot study

ObjectivesTo analyze the language of letters of recommendation for pediatric otolaryngology fellowship for possible gender and/or racial bias. To refine methodology for future investigation of gender and language within letters of recommendation for pediatric otolaryngology fellowship. MethodsPracticing pediatric otolaryngologists were recruited as survey participants. A total of 10 letters of recommendation were screened to represent an equal number of applicants who identify as male and female and an equal number of applicants who identify as White and applicants who identify as Black, Indigenous, or a Person of Color. Names, pronouns, location, and other identifying information were removed from the letters. Survey participants were asked to read each letter and categorize the applicant's gender and race as well as that of the letter writer. The letters were also subject to analysis by WordStat linguistic software. ResultsA total of 35 pediatric otolaryngologists participated in the survey. Participants assigned gender of the applicant correctly in 43 % of letters and gender of the letter writer correctly in 53 % of letters. Race of the applicant was judged correctly in 65 % of letters. Participants indicated words such as caring, hard-working, and prepared led them to assume the applicant was female, which was corroborated by linguistic analysis. No such words were identified as predictive of race. ConclusionThis pilot study suggests that while there are certain linguistic associations with gender among applications to pediatric otolaryngology fellowship, they may not impact biases held by the reader. This is relevant given a recent rise in the number of female applicants to this subspecialty and ongoing unfilled fellowship positions. Future studies are needed to determine if such associations exist and ultimately affect match potential and success within the field. These studies will require analysis of more letters and with more survey respondents, along with a higher in-depth linguistic analysis. Lay summaryThis pilot study aims to evaluate letters of recommendation for pediatric otolaryngology fellowship applications for gender bias with the goal of outlining future studies. There were certain words associated with a given gender, which could impact applicants’ potential to match. Level of evidenceNA.

Exploring Deep Learning Models for Lyric Generation and Addressing Biases in Word Embeddings

The aim of this project is to explore the performance of different model architectures (such as RNN, LSTM, GRU) by generating lyrics using deep learning models, and to use the Word2Vec model for distributed semantic analysis to understand semantic phenomena and potential biases in word embedding models. The experimental results show that LSTM and GRU perform better than traditional RNN models when processing long sequence data. In addition, by analyzing word embeddings, we revealed potential gender and racial biases and proposed corresponding solutions.

Assessment of Racial Bias within the Risk Analysis Index of Frailty

Objective: Our objective was to assess potential racial bias within the Risk Analysis Index (RAI). Background: Patient risk measures are rarely tested for racial bias. Measures of frailty, like the RAI, need to be evaluated for poor predictive performance among Black patients. Methods: Retrospective cohort study using April 2010–March 2019 Veterans Affairs Surgical Quality Improvement Program and 2010–2019 National Surgical Quality Improvement Program data. The performance of the RAI and several potential variants were compared between Black and White cases using various metrics to predict mortality (180-day for Veterans Affairs Surgical Quality Improvement Program, 30-day for National Surgical Quality Improvement Program). Results: Using the current, clinical threshold, the RAI performed as good or better among Black cases across various performance metrics versus White. When a higher threshold was used, Black cases had higher true positive rates but lower true negative rates, yielding 2.0% higher balanced accuracy. No RAI variant noticeably eliminated bias, improved parity across both true positives and true negatives, or improved overall model performance. Conclusions: The RAI tends to predict mortality among Black patients better than it predicts mortality among White patients. As existing bias-reducing techniques were not effective, further research into bias-reducing techniques is needed, especially for clinical risk predictions. We recommend using the RAI for both statistical analysis of surgical cohorts and quality improvement programs, such as the Surgical Pause.

Abstract A041: Assessing the impact of neighborhood structural racism on survival for African American men with prostate cancer; a population-based cancer registry study across eight US states

Abstract Background: African American (AA) men are more than twice as likely to die of prostate cancer (PC) compared to non-Hispanic White men in the US. Among AA men, we examined survival after PC diagnosis by neighborhood-level structural racism and whether its effects are mediated by neighborhood socioeconomic status (nSES). Methods: We pooled data for 133,241 AA men diagnosed with PC from 2000-2013 from ten population-based cancer registries across eight states (CA, Detroit, FL, GA, LA, NJ, NY, TX) of the RESPOND Study (Research on Prostate Cancer among African American Men). Residential addresses at PC diagnosis were geocoded and appended to census block-group measures of structural racism (contemporary redlining, racial/ethnic segregation typology, and racial bias in mortgage lending) and nSES, an index comprising measures of income, education, occupation, employment, and housing. We used Cox models and the %mediation SAS macro for causal mediation to assess overall and PC- specific survival according to structural racism and to quantify the extent to which survival differences were mediated by nSES. Models were adjusted for diagnosis year and age, state of residence, and marital status. Results: There were 46,830 deaths overall with 12,272 PC-specific deaths among the study population. Greater mortality risk was observed among AA men residing in neighborhoods in the highest compared to lowest quartile of redlining (total effect) for both overall survival (Hazards ratio [HR]=1.26; 95% CI=1.22, 1.31) and PC-specific survival (HR=1.30; 95% CI=1.21-1.39). Neighborhood SES mediated 59% and 47% of the effect of redlining, resulting in natural direct effects of HR=1.11 (95% CI=1.05, 1.17) and HR=1.16 (95% CI=1.04, 1.29) for overall and PC-specific survival, respectively. Compared to predominantly White neighborhoods, AA men residing in neighborhoods with all other racial/ethnic typologies had greater overall and PC-specific mortality risk, with largest risk for neighborhoods with predominantly AA or mixed with Hispanic and AA residents. Neighborhood SES completely mediated the effect of racial/ethnic typology on survival for all typologies except that of mixed Hispanic and Black residents (37% and 25% mediation by nSES for overall and PC-specific survival, respectively). There was no association between racial bias in mortgage lending and survival. Results from models additionally adjusted for prognosis (Prostate Cancer Cohort Consortium [PC3] classification of aggressive disease) and treatment (receipt of surgery, radiation, chemotherapy, or hormone therapy) were similar. Conclusions: Neighborhood SES explains substantial portions of the impact of redlining and racial/ethnic typology on overall and PC-specific survival among AA men across eight states in the US. Remaining survival differences for the most redlined neighborhoods and neighborhoods with a mix of Hispanic and Black residents independent of nSES suggest alternative pathways for the impact of neighborhood structural racism on mortality risk for AA men with PC. Citation Format: Mindy C. DeRouen, Meera Sangaramoorthy, Katherine Lin, Annie Vu, Dan Meltzer, Pushkar Inamdar, Yuhong Zhou, Kevin Ward, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Beebe-Dimmer, Anshu Shrestha, Aaron P. Thrift, Margaret Gates-Kuliszewski, Peter Kanetsky, Tamara Lotan, Anthony Colombo, David Conti, Christopher Haiman, Ann Hamilton, Kirsten Beyer, Joseph Gibbons, Iona Cheng, Salma Shariff-Marco, Scarlett Gomez. Assessing the impact of neighborhood structural racism on survival for African American men with prostate cancer; a population-based cancer registry study across eight US states [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A041.

The activism of David Dennis

In 2019, Canadian First Nations activist David Dennis challenged the 6-month alcohol abstinence requirement when he was denied placement on the liver transplant list. While Mr. Dennis was eventually placed on the waitlist, he passed away before receiving a transplant. As Canadian transplant programs seek to reduce healthcare inequities experienced by Indigenous patients—First Nations, Métis, and Inuit—this analysis sheds much needed light on the inconvenient truths of settler colonialism and the need for impactful reconciliation in Canadian healthcare. This analysis examines Dennis’ activism and his argument about the inequities in transplant medicine experienced by Indigenous patients. Dennis’ activism highlighted how the Canadian healthcare systems underserve Indigenous populations, including in transplant medicine; fails to address racial biases within healthcare; and does little to consider and address negative inter-generational determinants of health that perpetuate poor health outcomes for Indigenous populations. This analysis provides direction to decision makers in transplant and additions medicine, to Ministries of Health, and to First Nations, Métis, and Inuit healthcare leaders for improved healthcare for Indigenous patients living with alcohol-related liver disease. Without concerted and coordinated efforts to address the needs of this patient population, we will continue to see rapid deterioration of their health status and high rates of mortality will persist among this patient population.

The Looking Glass Framework: Using Virtual Reality and Video Game Simulations to Improve Clinical Decision-Making and Reduce Healthcare Disparities in African Americans

A central mission of a healthcare organization is to provide equal access and excellent health outcomes to all populations, regardless of race and ethnicity. However, in the everyday practice of healthcare, organizations have fallen short of this primary tenant. Healthcare disparities in the Black community are well-documented in the literature. Although the reasons are multi-faceted, racial bias contributes to healthcare disparities, and eliminating those disparities should be a chief focus in improving the health and well-being of every community. This paper discusses healthcare disparities in the Black community and the complex cycle that enables gaps to persist despite best efforts among healthcare professionals, policymakers, and the general public. It concludes with a presentation of the Looking Glass Framework; an innovative onboarding and continuing education program using virtual reality and computer-based educational modules to reduce healthcare disparities within clinics, hospitals, and health systems.

The effect of social factors on eye movements made when judging the aesthetic merit of figurative paintings

In the present study we explore how social factors (group contact, individuating experience, implicit racial bias) influence the eye movements made during the visual exploration when judging their aesthetic merit of figurative paintings depicting White and Black sitters. An opportunity sample of participants visiting a gallery in Liverpool viewed ten artworks while their eye movements were recorded and completed a set of individual difference measures. The individual difference measures indicated self-report of art interest, social contact and individuating experience with both Black and White communities, and implicitly held racial bias. The results showed that, despite viewing the paintings for less time, the majority of participants reported paintings showing Black sitters as more interesting, emotionally moving, and pleasurable then those depicting White sitters. However, if a participant reported limited social contact with Black community, and a negative implicit racial bias against them, their rating of aesthetic merit of paintings showing Black sitters was reduced, viewing time increased, and fixations became more focused on faces. The influence of social factors on the viewing of paintings showing White sitters was limited to aesthetic rating. The results are discussed in terms of how social factors influence eye movements when viewing paintings in a real-world setting.

Developing Indigenous Cultural Safety in Law: Clinical Legal Education as a Method for Getting it Done

The imperative of law schools to embed Indigenous cultural safety in legal education is well established. This paper argues that clinical legal education can make an important pedagogical contribution to the development of Indigenous cultural safety within the study of law. Drawing on a case study of a legal clinic in Logan, Queensland, this paper details clinic teaching and learning activities, and highlights the role of yarning as a process for engaging cultural humility and understanding. A review of pedagogical theories is provided to demonstrate that multiple theoretical approaches support the clinical method as an effective vehicle for delivering transformative learning on issues such as institutional racial bias within law, the legal profession, and broader society. Clinic evaluation reveals strong cultural safety learning outcomes. This paper concludes with some observations about the clinic’s strengths and limitations, including the benefits of devolving autonomy for clinic design to Indigenous clinical supervisors, issues of scalability and funding for community stakeholders.

Unjust: Publishing Black and African American Children’s Books and School Availability

Traditional book publishing has a pronounced and unjust deficiency of Black and African American voices. White culture, thoughts, and rules are the standard in traditional publishing. Black and African American authors are not typically picked up by white-dominated publishing companies. In traditional publishing, mostly white literary agents are gatekeepers and acquisition editors shut the doors too frequently to non-white authors. Aspiring Black authors then resort to hybrid or vanity press companies that often use unscrupulous practices, charge exorbitant fees, accomplish little or low-quality work, and deny authors’ agency and full rights in the final disposition of a book. Because a majority of traditionally published children’s books featuring Black or African American stories or characters are written by or illustrated by non-Black people, the wide possibilities of adventure, celebrations, discovery, and friendship stories are not published for Black children. Instead, publishers favor stories about slavery, the civil rights movement, famous Black people, and hair tales as well as racially ambiguous characters. Regrettably, Black and African American culturally relevant stories written and illustrated by Black or African Americans are not readily available to children in school and library settings consistent with schools’ community or student demographics. This article shares research findings and viewpoints of Kathy Anderson and Karen Bowlding, two Black children’s book writers and publishing consultants who are also parents. Black and African American students encounter education and cultural injustice because of the practices of traditional publishing companies, educators, and librarians. Parents and guardians can ameliorate these issues with discernment and action. Foremost, publishing company decision-makers must acknowledge their own racial biases that deny representation and authenticity to all children in our US classrooms.

Racial Bias in Academia: An Audit Experiment Revealing Disparities in Faculty Responses to Prospective Students

Building on Milkman, Akinola, and Chugh (2015), this article presents data from an experiment conducted in Australia that included fictional emails from prospective students seeking a meeting with faculty members. The results show significantly different responses from faculty depending on the student’s name and association with a racialized group. While the study reveals evidence of racial bias, there is, contrary to previous studies, little evidence of gender bias. Additionally, the study concludes that gender or racial diversity at the university or discipline level is not associated with lower rates of bias. Additional exploratory analysis further examines the data for evidence of change processes, including the interaction of gender and racial diversity, and lower rates of bias among more junior academics.

De-Escalation With Low-Intensity Antecedent Strategies: Implications for Disproportionate Disciplinary Outcomes

Research suggests that a cycle of misbehavior can escalate quickly from corrections over minor rule infractions to threats of serious disciplinary consequences. There is also evidence that Black youth may be especially at risk for these escalations due to subtle or implicit racial bias, or cultural mismatches that may lead teachers to believe that minor rule violations, especially those that require subjective judgments, convey willful defiance or disrespect. In this article, we provide a brief overview of data confirming that Black students experience disproportionate disciplinary outcomes, explore the idea that one contributor to disproportionality may be cultural disconnects between adults and the student population, and consider the idea that many punitive disciplinary actions evolve from relatively minor classroom conflict, disruption, or rule violations. We argue that fairly simple, low-intensity antecedent strategies that show promise in defusing problems rather than exacerbating them may offer mitigation against disproportionate disciplinary outcomes.

Implicit Race Bias in Pediatric Patients: Understanding Patient Perspectives

Implicit Race Bias in Pediatric Patients: Understanding Patient Perspectives

Fairness in AI-Driven Oncology: Investigating Racial and Gender Biases in Large Language Models.

Large language model (LLM) chatbots have many applications in medical settings. However, these tools can potentially perpetuate racial and gender biases through their responses, worsening disparities in healthcare. With the ongoing discussion of LLM chatbots in oncology and the widespread goal of addressing cancer disparities, this study focuses on biases propagated by LLM chatbots in oncology. Chat Generative Pre-trained Transformer (Chat GPT; OpenAI, San Francisco, CA, USA) was asked to determine what occupation a generic description of "assesses cancer patients" would correspond to for different demographics. Chat GPT, Gemini (Alphabet Inc., Mountain View, CA, USA), and Bing Chat (Microsoft Corp., Redmond, WA, USA) were prompted to provide oncologist recommendations in the top U.S. cities and demographic information (race, gender) of recommendations was compared against national distributions. Chat GPT was also asked to generate a job description for oncologists with different demographic backgrounds. Finally, Chat GPT, Gemini, and Bing Chat were asked to generate hypothetical cancer patients with race, smoking, and drinking histories. LLM chatbots are about two times more likely to predict Blacksand Native Americans as oncology nurses than oncologists, compared to Asians (p < 0.01 and < 0.001, respectively). Similarly, they are also significantly more likely to predict females than males as oncology nurses (p < 0.001). Chat GPT's real-world oncologist recommendations overrepresent Asians by almost double and underrepresent Blacks by double and Hispanics by seven times. Chatbots also generate different job descriptions based on demographics, including cultural competency and advocacy and excluding treatment administration for underrepresented backgrounds. AI-generated cancer cases are not fully representative of real-world demographic distributions and encode stereotypes on substance abuse, such as Hispanics having a greater proportion of smokers than Whites by about 20% in Chat GPT breast cancer cases. To our knowledge, this is the first study of its kind to investigate racial and gender biases of such a diverse set of AI chatbots, and that too, within oncology. The methodology presented in this study provides a framework for targeted bias evaluation of LLMs in various fields across medicine.

Exploring the Impact of Race on Addressing Intimate Partner Violence in the Emergency Department.

Research highlights racial disparities among those experiencing intimate partner violence (IPV), yet little is known about disparities in addressing IPV in the emergency department (ED). This study was designed to examine variability in offering IPV universal education to adult caregivers across patient race within an urban pediatric ED and to explore provider attitudes regarding the role of race in this process. We conducted a mixed-methods study using quantitative data on rates of offering adult caregivers IPV universal education and rates of missing documentation for when IPV universal education was not offered to adult caregivers from January 2016 to December 2020. Analyses compared both rates by patient race listed in the electronic health record. We also conducted semistructured interviews with ED providers, which were qualitatively analyzed for common themes. Caregivers of Black patients were both more likely to have received IPV universal education compared with caregivers of White patients (31.1% vs 27.3%, P < 0.05) and more likely to lack a documented reason for not offering than caregivers of White patients (70.4% vs 53.9%, P < 0.05). Our semistructured interviews with nurses, nurse practitioners, and physicians in the pediatric ED were able to ascertain specific racial biases that may influence these disparities. Our study showed that racial disparities exist in both offering IPV universal education and documentation deferral in our pediatric ED. Combined analysis of our quantitative and qualitative data shows the importance of identifying biases that cause health disparities and increasing diversity among healthcare providers. The results of this study can be used to inform new methodologies for healthcare providers to address their personal biases and ensure that all caregivers visiting the ED are offered IPV support resources.

The Effects of the 2020 BLM Protests on Racial Bias in the United States.

The 2020 Black Lives Matter (BLM) protests in response to the murder of George Floyd highlighted the lingering structural inequalities faced by Black people in the United States. In the present research, we investigated whether these protests led to reduced implicit and explicit racial bias among White U.S. Americans. Combining data from Project Implicit, Armed Conflict Location Event Data Project (ACLED), Google Trends, and the American Community survey, we observed rapid drops in implicit and explicit measures of racial bias after the onset of the protests. However, both types of racial bias slowly increased again over time as (attention to) BLM faded. We use directed acyclic graphs to show under which assumptions causal inferences are warranted. We discuss our results in light of situational models of bias, their implications for protest movements, and raise questions about when and how social norms play a role in large-scale attitude change.