“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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».