Critical Review of the Content Validity of Miller Forensic Assessment of Symptoms Test (M-FAST)
Bibliographic record
Abstract
Background:The Miller Forensic Assessment of Symptoms Test (M-FAST) [1] was originally developed for detection of malingering of psychiatric symptoms in forensic settings, but it is now used frequently on other clinical groups such as post-accident patients or war veterans, to assess malingering of non-psychiatric medical symptoms, i.e., on patient groups and on symptoms for which the M-FAST was not validated in accordance with standards of the American Psychological Association (APA). Method:The M-Fast consists only of 25 items.We undertook a systematic review of all 25 items to evaluate their content validity, i.e., congruence with the intended goal to differentiate malingerers from legitimate patients. Results: With respect to detection of malingering of psychiatric symptoms, the M-FAST items list many legitimate psychiatric symptoms that are (rather perplexingly) scored as indicators of malingering. A few examples are as follows: auditory hallucinations ("voices") associated with autonomic signs of anxiety (Item 18) or with fear of leaving the room or home during such episodes (Item 22), hallucinations lasting for days (Item 6), and olfactory hallucinations (phantosmia) (Item 17), adverse changes of mood while suspecting to be plotted against (Item 3), a belief to have special powers with respect to sensory perception (Item 13), and delusional parasitosis (Item 20). An M-FAST item refers to "feeling depressed most of the time" (Item 2) and is also scored as indicator of malingering. With respect to detection of malingering of medical symptoms in survivors of motor vehicle accidents (MVA), examples of unduly contaminated item content include: depressed feelings (Item 2), tinnitus triggered or exacerbated over the duration of stressful interview (Item 25), intense nightmares that occur concurrently with weight loss (Item 12), neurological symptom of formication (Item 20), phantosmia (Item 17), and fluctuation of symptoms as if someone is "turning them on and off " (Item 14).Discussion and Conclusions: More than a half of M-FAST items have content that can be legitimately endorsed by psychiatric patients, or those injured in MVAs, or by injured war veterans, but in the M-FAST, these items are erroneously scored as indicators of malingering.This can lead to high rates of false positives, e.g., 33% to 63% in the 2017 study by Weiss and Rosenfeld.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".