Assessing posttraumatic stress disorder symptom clusters with the Minnesota Multiphasic Personality Inventory‐3 in a forensic disability sample
Bibliographic record
Abstract
OBJECTIVE: Previous evidence indicates that scales from the Minnesota Multiphasic Personality Inventory (MMPI) family of instruments can measure self-reported posttraumatic stress disorder (PTSD) symptomology and differentiate symptom clusters, including in forensic disability assessments. However, limited research has examined assessment of PTSD symptoms with the MMPI-3, the most recent MMPI instrument. The goal of the current study was to identify the strongest MMPI-3 scale predictors of individual PTSD symptom clusters, measured via self-report. METHODS: = 42.98, SD = 10.87; 81.8% White), correlation, regression, and dominance analyses were performed to examine associations between scores on MMPI-3 scales and latent PTSD symptom cluster factors derived using confirmatory factor analyses from items of the Detailed Assessment of Posttraumatic Stress (DAPS), and to identify the strongest predictor of each symptom cluster when MMPI-3 scales were concurrently considered. RESULTS: Results indicate that conceptually expected MMPI-3 scale scores were meaningfully associated with PTSD symptom cluster factors, with the MMPI-3 Anxiety-Related Experiences (ARX) scale demonstrating the strongest and most consistent associations across symptom clusters. CONCLUSIONS: Results of the current study largely converge with previous empirical studies of self-reported PTSD symptoms in disability claimant settings with the MMPI instruments. Interpretive implications for the MMPI-3, limitations, and future research directions 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".