Colorism and Health Inequities among Black Americans: A Biopsychosocial Perspective
Bibliographic record
Abstract
The mechanisms generating skin-tone-based health inequities among ethnic Black Americans remain poorly understood. To address this gap, our study advances a novel biopsychosocial model of embodied colorism-related distress. We test this model with survey and biomarker data from a community sample of working-age Black adults from Nashville, Tennessee (2011-2014; N = 627). Relying on self-rated, interviewer-rated, and discordant skin tone measures, our analyses reveal that Black adults who perceive themselves as dark-skinned tend to have a lower sense of mattering and shorter telomeres, a biomarker of accelerated cellular degradation and aging, relative to their peers who perceive their skin to be lighter. These patterns hold across various social contexts and regardless of interviewer-rated skin tone, indicating that subjective skin tone may be a particularly robust gauge of colorism-related stress processes. Our study reveals critical and previously unexplored biopsychosocial mechanisms linking colorism to health inequity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".