The weaponization of medicine: Early psychosis in the Black community and the need for racially informed mental healthcare
Bibliographic record
Abstract
There is a notable disparity between the observed prevalence of schizophrenia-spectrum disorders in racialized persons in the United States and Canada and White individuals in these same countries, with Black people being diagnosed at higher rates than other groups. The consequences thereof bring a progression of lifelong punitive societal implications, including reduced opportunities, substandard care, increased contact with the legal system, and criminalization. Other psychological conditions do not show such a wide racial gap as a schizophrenia-spectrum disorder diagnosis. New data show that the differences are not likely to be genetic, but rather societal in origin. Using real-life examples, we discuss how overdiagnoses are largely rooted in the racial biases of clinicians and compounded by higher rates of traumatizing stressors among Black people due to racism. The forgotten history of psychosis in psychology is highlighted to help explain disparities in light of the relevant historical context. We demonstrate how misunderstanding race confounds attempts to diagnose and treat schizophrenia-spectrum disorders in Black individuals. A lack of culturally informed clinicians exacerbates problems, and implicit biases prevent Black patients from receiving proper treatment from mainly White mental healthcare professionals, which can be observed as a lack of empathy. Finally, we consider the role of law enforcement as stereotypes combined with psychotic symptoms may put these patients in danger of police violence and premature mortality. Improving treatment outcomes requires an understanding of the role of psychology in perpetuating racism in healthcare and pathological stereotypes. Increased awareness and training can improve the plight of Black people with severe mental health disorders. Essential steps necessary at multiple levels to address these issues are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".