“A Bias Recognized is A Bias Sterilized”: A Literature Review on How Biased Datasets Have Led to the Long-standing Misdiagnosing of People of Color (POC) and Female Patients
Bibliographic record
Abstract
Introduction: Health disparities disproportionately impact minority group patients. Various factors perpetuate health inequity, including socioeconomic status, prejudice and discrimination. Historically, sample biases favoring White males in healthcare literature have led to the underrepresentation of certain groups in scientific literature, particularly people of color (POC) and female populations. Many revolutionary studies in healthcare research have used biased samples, which challenges their generalizability to POC and female populations. This review explores the mechanisms by which these gaps in the literature have led to the misdiagnoses of POC and female patients in psychiatric and biomedical settings. Methods: A comprehensive literature review was conducted to investigate: (1) misrepresentation of minority groups in literature, (2) variation in the symptomatology and etiology of disorders and diseases in female and POC populations; and (3) biases within accepted diagnostic measures and criteria. Electronic databases such as PubMed, PsychINFO and Google Scholar were used to search key terms including ‘health inequity’, ‘cross-cultural validity’, ‘racial disparities’, ‘sex disparities’, ‘diagnostic delays’, ‘misdiagnosis’, ‘clinical heterogeneity’. Results: Eighty-seven studies were examined, and 38 studies were included in the review. Findings suggest that misclassification of group membership, poor conceptualizations of minority identities, inadequate understanding of symptomatology variation, exclusion of social context, lack of culturally sensitive approaches, biased diagnostic tools and an absence of diverse samples in historical datasets have resulted in a harmful deficit in minority representation within medical literature. Discussion: Bias in healthcare literature has led to the systematic underrepresentation of minority populations in medical research and contributes to the misdiagnosis and subsequent health inequities within these groups. Present findings emphasize the necessity to regard past health research with reasonable skepticism and a call for prioritization of inclusive and diverse research. Conclusion: This review sheds light on how to bridge the literature deficit caused by biased research through highlighting how minority populations are differentially impacted within the healthcare field and identifying factors that perpetuate these disparities. Further research on the examined factors must be conducted to develop approaches to mitigate misdiagnosis rates and subsequent health inequities among POC and female patients.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".