Socially-assigned race and health: a scoping review with global implications for population health equity
Bibliographic record
Abstract
Self-identified race/ethnicity is largely used to identify, monitor, and examine racial/ethnic inequalities. A growing body of work underscores the need to consider multiple dimensions of race - the social construction of race as a function of appearance, societal interactions, institutional dynamics, stereotypes, and social norms. One such multidimensional measure is socially-assigned race, the perception of one's race by others, that may serve as the basis for differential or unfair treatment and subsequently lead to deleterious health outcomes. We conducted a scoping review to systematically appraise the socially-assigned race and health literature. A systematic search of the PubMed, Web of Science, 28 EBSCO databases and 24 Proquest databases up to September 2019 was conducted and supplemented by a manual search of reference lists and grey literature. Quantitative and qualitative studies that examined socially-assigned race and health or health-related outcomes were considered for inclusion. Eighteen articles were included in the narrative synthesis. Self-rated health and mental health were among the most frequent outcomes studied. The majority of studies were conducted in the United States, with fewer studies conducted in New Zealand, Canada, and Latin America. While most studies demonstrate a positive association between social assignment as a disadvantaged racial or ethnic group and poorer health, some studies did not document an association. We describe key conceptual and methodological considerations that should be prioritized in future studies examining socially-assigned race and health. Socially-assigned race can provide additional insight into observed differential health outcomes among racial/ethnic groups in racialized societies based upon their lived experiences. Studies incorporating socially-assigned race warrants further investigation and may be leveraged to examine nuanced patterns of racial health advantage and disadvantage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".