Estimating posttraumatic stress disorder severity in the presence of differential item functioning across populations, comorbidities, and interview measures: Introduction to Project Harmony
Bibliographic record
Abstract
Multiple factor analytic and item response theory studies have shown that items/symptoms vary in their relative clinical weights in structured interview measures for posttraumatic stress disorder (PTSD). Despite these findings, the use of total scores, which treat symptoms as though they are equally weighted, predominates in practice, with the consequence of undermining the precision of clinical decision-making. We conducted an integrative data analysis (IDA) study to harmonize PTSD structured interview data (i.e., recoding of items to a common symptom metric) from 25 studies (total N = 2,568). We aimed to identify (a) measurement noninvariance/differential item functioning (MNI/DIF) across multiple populations, psychiatric comorbidities, and interview measures simultaneously and (b) differences in inferences regarding underlying PTSD severity between scale scores estimated using moderated nonlinear factor analysis (MNLFA) and a total score analog model (TSA). Several predictors of MNI/DIF impacted effect size differences in underlying severity across scale scoring methods. Notably, we observed MNI/DIF substantial enough to bias inferences on underlying PTSD severity for two groups: African Americans and incarcerated women. The findings highlight two issues raised elsewhere in the PTSD psychometrics literature: (a) bias in characterizing underlying PTSD severity and individual-level treatment outcomes when the psychometric model underlying total scores fails to fit the data and (b) higher latent severity scores, on average, when using DSM-5 (net of MNI/DIF) criteria, by which multiple factors (e.g., Criterion A discordance across DSM editions, changes to the number/type of symptom clusters, changes to the symptoms themselves) may have impacted severity scoring for some patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".