Investigating the M‐FAST: psychometric properties and utility to detect diagnostic specific malingering
Bibliographic record
Abstract
This study examined the ability of the M-FAST to differentiate a group of undergraduate students simulating one of four DSM-IV diagnoses (n = 190; schizophrenia, major depressive disorder, bipolar disorder, and posttraumatic stress disorder) and a clinical comparison sample drawn from previous M-FAST studies comprising individuals with the same diagnosis (n = 142). Across all diagnostic conditions, the simulators obtained higher M-FAST total scores than the clinical comparisons, and the rare combinations scale was equal or superior to the total score at differentiating the groups. The M-FAST was most efficient at distinguishing feigned from bona fide schizophrenia. Although the internal consistency of the total score was high (alpha = 0.88), inter-item correlations were lower than values reported in previous research. Lastly, given the importance of base rate considerations in the evaluation of diagnostic instruments, it was notable that the M-FAST was able to identify malingerers even at relatively low base rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".