Discrimination and wellbeing are differentially related to pain severity for the racially marginalized
Bibliographic record
Abstract
OBJECTIVE: This study examines the relationship between racial discrimination and physical pain outcomes. METHODS: A geographically representative sample of 887 individuals was recruited online through CloudResearch from diverse racial backgrounds, including Black/African American, Latine/Hispanic American, Asian American, and White/European American adults. Participants completed measures on racial and ethnic discrimination, racial microaggressions, pain severity, depression symptoms, and coping styles. Statistical analyses included multiple regression and mediation models. RESULTS: Our findings indicate that racialized participants experienced greater ethnic discrimination and racial microaggressions compared to their non-Hispanic White counterparts. Hispanic/Latine participants also reported greater pain severity than other groups. Lifetime experiences of discrimination, depression symptoms, avoidant coping style, and age emerged as significant predictors of pain severity, while mediation analyses revealed that lifetime discrimination partially mediated the relationship between race/ethnicity and pain severity for racially marginalized participants, compared to non-Hispanic White participants. Furthermore, greater reliance on avoidant coping combined with greater lifetime discrimination experiences was associated with increased severity of pain. CONCLUSIONS: The findings indicate how racism may result in worse pain outcomes in people of color, with potentially amplified adverse effects for those who engage in avoidant coping. While therapeutic interventions targeting avoidance may benefit racialized individuals, ultimately, the results highlight the critical need for large-scale policy interventions targeting racial discrimination to improve health equity and reduce the burden of pain among racialized populations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".