The utility and comparative incremental validity of the MMPI-2 and Trauma symptom Inventory validity scales in the detection of feigned PTSD.
Bibliographic record
Abstract
The authors examined the comparative predictive capacity of the Trauma Symptom Inventory (TSI) Atypical Response Scale (ATR) and the standard set of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) fake-bad validity scales (i.e., F, F-sub(B), F-sub(p), FBS) to detect feigned posttraumatic stress disorder (PTSD). Remitted trauma victims (n = 60) completed the TSI and MMPI-2 under standard (honest) instructions and then were randomly assigned to 1 of 2 experimental conditions (noncoached/validity scale coached) in which they were administered these instruments again with instruction to fake PTSD. These test protocols were compared with TSI and MMPI-2 results from workplace injury claimants with PTSD (n = 84). The ATR and FBS were able to distinguish only the noncoached participants instructed to fake from the PTSD claimants; in contrast, the F, F-sub(B), and F-sub(p) scales were able to distinguish both the noncoached and the validity-scale-coached participants from the PTSD claimants. F, F-sub(B), and F-sub(p) always outperformed the ATR and FBS; neither the ATR nor the FBS was able to add incremental predictive variance to that of F, F-sub(B), or F-sub(p).
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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 | 0.020 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".