Can the MMPI-2 Validity Scales Detect Depression Feigned by Experts?
Bibliographic record
Abstract
Major depression is one of the most frequently presented disorders for claims of psychiatric disability. Evidence also suggests that many individuals making claims of disability exaggerate or even fabricate mental illness. These facts suggest that the detection of feigned depression is an important task in psychiatric disability claim assessments. In this study, the capacity of a number of MMPI-2 validity scales and indicators to detect feigned depression was examined. Twenty-three mental health professionals with specific expertise and significant experience in assessing and treating major depression were asked to complete the MMPI-2 as if they were suffering from major depression. The MMPI-2 protocols of this sample were compared to those of a sample of patients diagnosed with major depression. Results indicated that the validity scales F, back F (FB), and the Dissimulation scale (Ds) were highly successful at distinguishing MMPI-2 protocols of feigned depression from bona fide depression. Replicating results from previous studies, however, FB proved most effective, outperforming all other validity scales and indicators, including F and Ds. These findings suggest that even experts are unable to feign major depression successfully on the MMPI-2, and that the FB scale might be the most effective indicator for detecting feigned depression.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".