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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".