Measurement nonequivalence of the Clinician-Administered PTSD Scale by race/ethnicity: Implications for quantifying posttraumatic stress disorder severity.
Bibliographic record
Abstract
Research studies suggest racial/ethnic differences in posttraumatic stress disorder (PTSD) diagnosis and symptom severity. Few studies to date, however, have examined the extent to which these findings are due to differences in measurement properties of existing PTSD scales. This study examined measurement equivalence across race/ethnicity in the Clinician-Administered PTSD Scale (CAPS) by testing for differential item functioning (DIF) in the item response theory (IRT) framework. Participants were 506 trauma-exposed women (M = 39.41 years, SD = 8.94) who participated in the National Drug Abuse Treatment Clinical Trials Network Women and Trauma Study. PTSD severity score estimates were improved upon as part of IRT estimation incorporating symptom "weights" (i.e., factor loadings) and group-specific DIF. Six symptoms from the CAPS showed DIF, with the majority of differences in measurement driven by White/African American and White/Latina differences, particularly for (a) avoidance of thoughts and (b) a sense of foreshortened future. Despite both racial/ethnic minority groups being slightly (not significantly) more likely to receive a PTSD diagnosis, African Americans (p = .014; Cohen's d = -.22) and Latinas (p < .001; d = -.73) had significantly lower PTSD severity scores than Whites as estimated under IRT with group-specific DIF. Examination of PTSD severity scores based on symptom counts revealed these differences were either dampened (White/Latina difference d = -.39) or entirely negated (White/African American difference d = -.08). The findings suggest the importance of considering differences in symptom relevance across race/ethnicity and their impact on capturing symptom severity parallel to diagnostic criteria. Implications for clinical practice are discussed. (PsycInfo Database Record (c) 2020 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 | 0.111 | 0.263 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".