Cross-disciplinary rapid scoping review of structural racial and caste discrimination associated with population health disparities in the 21 <sup>st</sup> Century
Notice bibliographique
Résumé
ABSTRACT A cross-disciplinary rapid scoping review was carried out generally following PRIMA-SCR protocol to examine historical racial and caste-based discrimination as structural determinants of health disparities in the 21 st century. We selected 48 peer-reviewed full-text articles available from the University of Memphis Libraries database search, focusing on three selected case-study countries-the United States (US), Canada and Nepal. Authors read each article, extracted highlights and tabulated the thematic contents on structural health disparities attributed to racism or casteism. Results linked the historical racism/casteism to health disparities occurring in Blacks and African Americans, Native Americans and other ethnic groups in the US, in Indigenous peoples and other visible minorities in Canada, and in Dalits of Nepal, a population racialized by caste, grounded on at least four foundational theories explaining structural determinants of health disparities. The evidence from the literature indicates that genetic variations and biological differences (e.g., disease occurrence) occur within and between races/castes for various reasons (e.g., random gene mutations, geographic isolation, and endogamy). However, historical races/castes as socio-cultural constructs have no inherently exclusive basis of biological differences. Disregarding genetic discrimination based on pseudo-scientific theories, genetic testing is a valuable scientific means to achieve better health of the populations. Epigenetic changes (e.g., weathering – early aging of racialized women) due to DNA methylation of genes among racialized populations are markers of intergenerational trauma due to racial/caste discrimination. Likewise, chronic stresses resulting from intergenerational racial/caste discrimination cause ‘allostatic load,’ characterized by an imbalance of neuronal and hormonal dysfunction, leading to occurrences of chronic diseases (e.g., hypertension, diabetes, mental health) at disproportionate rates among racialized populations. Major areas identified for reparative policy changes and interventions for eliminating health impacts of racism/casteism include health disparity research, organizational structures, programs and processes, racial justice in population health, cultural trauma, equitable healthcare system, and genetic discrimination. Highlights Research on the relationship between structural racism and population-level health outcomes and on the health impacts of policy changes and interventions has largely overlooked caste, which is a system of racialization that pre-dates US racial categories. A cross-disciplinary global ‘caste’ approach is adopted to examine various forms of historical decent-based racial and caste discrimination in three case-study countries. Major theories and praxis explaining research and experiences of structural racial and caste discrimination impacting health disparities are consolidated to synthesize a unified body of knowledge for the 21 st century. While genetic variations occur naturally, they do not inherently contribute to the social constructs of race or caste. Reparative policy changes and interventions are necessary to eliminate deeply entrenched structural health disparities rooted in racism and casteism.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,035 | 0,029 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».