Racism exposure and trauma accumulation perpetuate pain inequities—advocating for change (RESTORATIVE): A conceptual model.
Bibliographic record
Abstract
Experiences of racism occur across a continuum from denial of services to more subtle forms of discrimination and exact a significant toll. These multilevel systems of oppression accumulate as chronic stressors that cause psychological injury conceptualized as racism-based traumatic stress (RBTS). RBTS has overlapping symptoms with posttraumatic stress disorder (PTSD) with the added burden that threats are constantly present. Chronic pain is a public health crisis that is exacerbated by the intersection of racism and health inequities. However, the relationship between RBTS and pain has not yet been explored. To highlight how these phenomena are interlinked, we present Racism ExpoSure and Trauma AccumulatiOn PeRpetuate PAin InequiTIes-AdVocating for ChangE (RESTORATIVE); a novel conceptual model that integrates the models of racism and pain and demonstrates how the shared contribution of trauma symptoms (e.g., RBTS and PTSD) maintains and perpetuates chronic pain for racialized groups in the United States. Visualizing racism and pain as "two halves of the same coin," in which the accumulative effects of numerous events may moderate the severity of RBTS and pain, we emphasize the importance of within-group distinctiveness and intersectionality (overlapping identities). We call on psychologists to lead efforts in applying the RESTORATIVE model, acting as facilitators and advocates for the patient's lived experience with RBTS in clinical pain care teams. To assist with this goal, we offer suggestions for provider and researcher antiracism education, assessment of RBTS in pain populations, and discuss how cultural humility is a central component in implementing the RESTORATIVE model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".