Dr. Christopher Williams developed the Critical Race Framework critical appraisal tool. Dr. Williams is a visionary leader in equity leadership and transformative public health practice. As the Founding Director of Public Health Liberation, he applies transdisciplinary synthesis to address challenges in what he terms the "Public Health Economy" - an original conceptualization of structural determinants of health. Dr. Williams combines research, theory-building, and community leadership to accelerate health equity, calling for a disciplinary shift in public health.

The Critical Race Framework is the first critical appraisal tool in the public health literature specifically on the use of race in research. It assumes that users have high familiarity with each key area of critical appraisal: reliability, validity, internal validity, and external validity. To download a copy, complete the form below.

Fuemmeler et al. (2025) conducted a study on cancer control behaviors, but assumed Black monolithism throughout the US South, Midwest, and Mid-Atlantic. "This study typifies poor research practices on race essentialism in public health research despite its drag on data quality and practical significance," said Dr. Christopher Williams, Principal Investigator for the Critical Race Framework Study

This essay uses three manuscripts and an article critique by Dr. Christopher Williams to illustrate the broken nature of the peer review system, where methodological flaws like wide confidence intervals were not caught, potentially due to bias, lack of transparency, and human error. These failures risk misleading public health policy, underscoring the need for transparent, rigorous, and accountable peer review systems to ensure the integrity of scientific research, especially on sensitive topics like maternal health.

Accurate race classification is critical, even in simulated datasets. The idealized design amplifies the visibility of misclassification effects, suggesting real-world impacts could be more severe. Researchers should use validated race data and consider correction methods (Buonaccorsi, 2010). Ethically, simulating race data requires transparency to avoid perpetuating stereotypes or misinforming policy.

Low validity in race classification, simulated as systematic misclassification, significantly impairs linear regression models predicting QoL. Low-validity models show reduced R-squared (0.811–0.798 vs. 0.823–0.810) and weakened Category3 coefficients, masking disparities. Continuous predictors remain robust, but ethical concerns highlight the need for accurate race data, even in simulated studies. Future research should test correction methods and real-world misclassification patterns.

The noisier dataset’s absent race effects could hide disparities, especially for groups like Category3, potentially misleading policy or research (Krieger, 2012). The cleaner dataset’s race coefficients, while significant, may overstate differences if misclassification isn’t addressed. Both cases stress the need for transparent data generation to avoid misrepresenting or erasing disparities in simulated studies.

In their 20-year follow-up on the Insitute of Medicine (IOM) 2003 Unequal Treatment report, the National Academies of Sciences, Engineering, and Medicine (NASEM) concluded in 2024, "The resulting report, Ending Unequal Treatment: Strategies to Achieve Equitable Health Care and Optimal Health for All, that "America has made little progress in advancing health care equity and that racial and ethnic inequity remain fundamental flaws in the health care system" (Consensus Study Report Highlights). In a NIH RePORTER search for "racial disparities" or "ethnic disparities" (all post award statuses excluding "terminated") for grants between 2004-2025, FY total costs totaled $4,158,786,813. Given the NASEM finding, it is likely that there was considerable waste in NIH funding on racial and ethnic disparities research.

Three AI models - Grok, Gemini, and ChatGPT - determined that the Public Health Liberation theory was highly beneficial as a framing to examine a PBS story on the Trump administration's public health policy impact. Both Gemini and ChatGPT did not consider any public health theory to offer a better framing. Grok concluded that only the Social Ecological Model was superior to Public Health Liberation theory.

The 2017 film, Get Out, was widely regarded as a social critique of racism. However, a major implication of the film was that the bodily manipulation of Chris and other characters has universal applicability beyond race. None of the four major AI models (Grok, Gemini, CoPilot, and ChatGPT) identified the universal implication of the exploitative technology of body snatching, following a user request to generate 300 major implications from the film. As Grok concluded, "This oversight raises profound questions about AI’s ability to interpret complex narratives, the perpetuation of narrow cultural perspectives, and the broader understanding of systemic oppression in society."

Jordan Peele’s Get Out presented a masterclass in genre-bending horror, unmasking white liberal racism through a deeply symbolic narrative. At first glance, the film is a racial allegory—but it’s far more. It is a cautionary tale about control, commodification, and how quickly dehumanization can be normalized. One major implication, largely missed by AI models and some critics, is this: if it can happen to the Black protagonist, it can happen to anyone. The core terror is not just that racism exists, but that systems of domination—once normalized—can be deployed on any body. This point takes on urgent relevance when we place it in dialogue with Public Health Liberation (PHL) theory, especially as the U.S. enters a new chapter under a second Trump administration in 2025.

The American Public Health Association (APHA) calls for refining racial and ethnic data collection but falls short of aligning collection and analysis with standards of scientific rigor. The Critical Race Framework Study extensively discussed the problems with current practices on racialization. Dr. Williams does not support APHA's recommendations because they are not fully situated in scientific reasoning and perpetuates a notion of race essentialism that has no place in modern science.

This study analyzed 1094 PubMed articles (2020–present) to identify longitudinal studies involving African American populations that provide active care or interventions we found 156 studies (14.3%) that meet these criteria. These studies represent a minority compared to the 86% (938 studies) that are purely observational, documenting health disparities without providing care.

This methodological review highlights critical gaps in the use of race in HIV-related systematic reviews of BMSM in the U.S. The lack of conceptual clarity, measurement rigor, and intersectional analysis undermines the scientific validity of these studies and perpetuates assumptions of homogeneity. To advance racialized research and address HIV disparities effectively, future studies must adopt rigorous, intersectional approaches that account for the complexity of race and identity among BMSM. Such efforts will enhance the precision of findings and inform more equitable public health interventions.

Racial health disparities articles that were published in 2025 scored low marks using the Critical Race Framework, according to Grok. Ten articles drawn randomly from the literature had "no discussion" or "low-quality discussion" ratings for at least 13 out of the 20 items in the CR Framework.

The dissemination of studies with crude racial categorization accompanied by fundings that elicit shock must end. Such papers can often be used to attract media and funder attention and to increase a researcher's profile without contributing meaningfully to scientific knowledge.

ChatGPT developed a 10-item rubric, rating Dr. Williams' Critical Race Framework Study higher or equal to the National Academies' Rethinking Race and Ethnicity in Biomedical Research (2025) in 7 out of 10 items. 

Dr. Williams answers questions to clarify the use of race, scientific integrity, and tool readiness. "I differentiate between critiquing race and advocating for its abandonment by insisting that researchers measure what they are intending to measure, correct for error, or ditch it."
Image: Grok (xAI)

A study in Washington, DC’s Southwest-Waterfront used cluster analysis to reveal how gentrification impacts mental health, identifying three resident groups with distinct perceptions of neighborhood change. Negative Perceivers, often renters, reported significantly worse mental health (30% poor/fair) compared to Positive Perceivers (5%), highlighting gentrification’s role as a social determinant of health.

This article analysis of Indices of neighborhood disadvantage and individual cancer control behaviors among African American adults by Fuemmeler et al. (2025) critiques common assumptions and practices in health disparities research. "The authors' assumptions of a Black monolith pales in comparison to other critical flaws in data analysis and interpretation," said Dr. Christopher Williams. "The uncritical view of the analytical treatment of race is part and parcel of a much larger systemic issue that needs significant improvement."

Parker and colleagues' meta-synthesis, Religious/Spiritual Struggles and Mental Health Among Black Adolescents and Emerging Adults: A Meta-synthesis, typifies poor practices in US research that assume Black monolithism. This study summarizes the literature on religious/spiritual struggles among Black Americans but overgeneralizes and overinterpret findings.

"Overall, research on the effects of structural-level interventions to address health inequities is lacking, and the evidence base would benefit from well-designed studies on upstream policy interventions that affect the structural determinants of health and health inequities and improve daily living conditions."

The postmodern Health Energy theory posits that health is a single-sample probabilistic state from an unknown probabilistic curve shaped by an ever-shifting health energy field that encompasses immeasurable or unquantifiable health-promoting sources of vitality like love, faith, resilience, community, and optimism.

Cardiologist on Love and Health: "We know and when we're celebrating love, especially on Valentine's Day, that it's not just a Hallmark holiday. Having support where it be friends, a partner, family members, but being loved is going to help with that social connection. It's going to help decrease depression and ultimately it's going to help your health...to help you live longer and happier and healthier."

Image: Sora-generated image from "Show life without vitality"

"Overall, research on the effects of structural-level interventions to address health inequities is lacking, and the evidence base would benefit from well-designed studies on upstream policy interventions that affect the structural determinants of health and health inequities and improve daily living conditions."

Noun

gattopardismo m (plural gattopardismos)

1. (politics) the political philosophy or strategy of advocating for revolutionary changes, but in practice only superficially modifying existing power structures

The concept of gattopardismo involves seeing political change as superficial window dressing - a change in appearance such as party leadership to maintain the status quo without structural change. Gattopardismo can be applied to non-political reform that appears to address equity but lack change to affect the social and material condition of affected populations.

The Centering Racial Equity Throughout Data Integration Toolkit 2.0, published by Actionable Intelligence for Social Policy (AISP) in February 2025, aims to transform data integration to confront systemic racism and promote equitable outcomes. However, the AISP toolkit and its applications—like the Data Hub, Opportunity Youth Use Case, Maternal and Child Health Use Case, and EiPLC—exhibit gattopardismo in Baltimore’s context. Procedural outputs and persistent disparities undermine their equity intent, creating an illusion of change. While community engagement offers potential, the lack of measurable impact reinforces the critique that these initiatives prioritize appearance over substance, necessitating stronger action to address structural racism.

Five AI models - ChatGPT, Grok, Gemini, DeepSeek, and Perplexity - were asked to rank public health theories from best to worst. Public Health Liberation theory ranks highly among most models.

The 2022 US Department of Housing and Urban Development (HUD) audit of the District of Columbia Housing Authority underscored chronic mismanagement of the largest landlord in Washington, DC, a public agency responsible the public housing portfolio and voucher system. While the report focused on federal housing regulations, the public health implications of the dysfunctional agency were severe.

Engage with the Critical Race Framework by taking this quiz! 

The Critical Race Framework Study: Standardizing Critical Evaluation for Research Studies That Use Racial Taxonomy is likely to deviate from common practices and teaching by challenging the modern uses and meaning of race in research. To understand the shortcomings of a global notion of race, take the quiz.

Dr. Christopher Williams, principal investigator of the Critical Race Framework Study: Standardizing Critical Evaluation for Research Studies That Use Racial Taxonomy developed, with help from Grok, a 30-question knowledge check. With the Critical Race Framework study exceeding 200 pages, this quiz ensures that readers have a firm grasp of the study's goal, methods, and limitations. 

The study of health inequities has produced robust theoretical frameworks, with Amartya Sen’s (2002) exploration of health equity, Ana V. Diez Roux’s (2012) analysis of health disparities, and Zinzi D. Bailey et al.’s (2017) focus on structural racism and health inequities standing as pivotal contributions. Each manuscript offers distinct insights into the causes and solutions for health inequities, yet they leave critical gaps unaddressed, particularly in community agency, transdisciplinary integration, liberatory principles, and intersectional perspectives. Public Health Liberation (PHL) theory, as articulated by Christopher Williams et al. (2022), emerges as a transdisciplinary framework that fills these gaps.

Dr. Christopher Williams, principal investigator for the Critical Race Framework study, leveraged Grok, a large language model (LLM), to develop steps in computing error and adjusted estimates due to attenuated racial variables used in research. 

The Critical Race Framework (CRF) Quantitative Critical Appraisal Aid (QCAA) is a six-step approach that uses reported regression β's, β confidence intervals, SE and psychometric or reliability statistics (Cronbach's alpha) to compute confounding bias, reliability and validity error. Since accessing the datasets and exact methodologies in data analysis to assess error, the tool can enable public health researchers to approximate adjusted coefficients and CIs to more accurately align with the weaking effects of racial variables in research.

Grok finds that Aiello et al. study’s use of race/ethnicity introduces significant biases (50–100% distortion), rendering race-adjusted estimates shaky. Reliability and validity issues undermine the covariate’s effectiveness, while internal and external validity flaws inflate primary exposure effects (e.g., CAIDE, total Tau). These biases, consistent with our prior CR Framework applications (March 10, April 17, 2025), limit interpretability and policy relevance. Public health research must adopt rigorous race measurement, as advocated by the CR Framework, to ensure valid Alzheimer’s risk factor studies.

Public Health Realism (PHR), a theory of Public Health Liberation theory, lacked strong empirical evidence when it was published in 2022. This research brief utilizes artificial intelligence models (Grok, Perplexity, Gemini, and ChatGPT) to assess validity of PHR theory. These models assessed thirteen (13) or 81% of the 16 Principles of Public Health Realism as "strong" or "very strong" in validity, relying on theoretical grounding. 

Results support findings from original study, raising alarm about race essentialism in public health research.

Dr. Williams reflects on practices in public health research that undermine quality and interpretability.