A Psychometric Investigation of Racial Trauma Symptoms Using a Semi-Structured Clinical Interview With a Trauma Checklist (UnRESTS)
Bibliographic record
Abstract
The term racial trauma is used to describe the cumulative distressing and traumatizing effects of racism in all of its forms, and it closely resembles the construct of posttraumatic stress disorder (PTSD). This investigation aims to increase our understanding of racial trauma by comparing the characteristics of those with a clinically-relevant diagnosis of racial trauma to those without, based on the findings of a clinical semi-structured interview and symptom checklist for assessing racial trauma, the University of Connecticut Racial Ethnic Stress and Trauma Survey (UnRESTS), administered to a diverse group of adults (N = 97). This paper extends prior work on racial trauma by examining the correlations between racial trauma and validated self-report measures of discriminatory distress, controlling for racialization. We examine the correlation between a clinically-relevant diagnosis of racial trauma and racial/ethnic identity. We also compare racism-related PTSD symptoms in those with and without racial trauma to inform clinical assessment. Finally, we examine the factor structure of racial trauma symptoms using the 24 items from the UnRESTS PTSD symptom checklist and compare these to current DSM-5 models. The structure of racial trauma symptoms differed from the DSM-5 4-factor model, as do other PTSD models in the research literature. Clinical and research implications are discussed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".