A review of the inclusion of ethnoracial groups in empirically supported posttraumatic stress disorder treatment research.
Bibliographic record
Abstract
OBJECTIVE: Empirically supported treatments (ESTs) have been criticized for lack of ethnoracial representation, which may limit the generalizability of findings for non-White patients. This study assessed ethnoracial representation in United States-based randomized controlled trials (RCTs) for three evidence-based treatments for posttraumatic stress disorder (PTSD)-Prolonged Exposure (PE), Cognitive Processing Therapy (CPT), and Eye-Movement Desensitization and Reprocessing (EMDR). METHOD: Representation was measured by explicit inclusion of people of color in published PTSD RCTs. Follow-up emails were sent to corresponding authors if full demographic information was not included in the reviewed manuscripts. Information concerning participant remuneration was collected for descriptive purposes. RESULTS: All three treatment modalities reported White participants as the majority in their sample. PE and CPT trials reported similar levels of ethnoracial diversity, while EMDR efficacy studies reported the least ethnoracial diversity. Across the reviewed studies, with few exceptions, we found low numbers of non-White participants in the majority of reviewed studies, which was compounded by poor or unclear methods of reporting ethnoracial information. CONCLUSIONS: This study demonstrates that the ESTs for PTSD are not adequately representative of the majority of non-White participants. Future RCTs should place a stronger emphasis on broad ethnoracial diversity in study participants to improve generalizability of findings. (PsycInfo Database Record (c) 2022 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 | 0.049 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".