One hundred and ten fundamentals of performance validity tests in neuropsychological forensic disability and related assessment II: Literature review
Bibliographic record
Abstract
The concepts underlying performance validity tests (PVTs) change in focus and, also, the research in their use in forensic neuropsychological assessment is burgeoning. First, we review definitions, testing, and counting procedures. As well, ethics related to assessment have changed, in addition to admissibility laws for court. Next, we examine test construction, and provide a pertinent example of a recently developed PVT, the Inventory of Problems-Memory (IOP-M). In terms of recent empirical PVT research, we enumerate 15 themes that organize the field. There are five themes on foundations, five on extensions, and five on applications. They include: (a) for foundations: PVT validation, PVT fail base rate, PVT cut scores, creating new tests, and embedded PVTs; (b) for extensions, novel PVT approaches, advanced PVT technologies, remote PVTs, demographic research and limitations, and multivariate approaches; and (c) for other applications, clinical applications, students, psychological injuries, extreme conditions, and combinations with symptom validity tests (SVTs). The ongoing research on PVTs support their use in forensic neuropsychological assessment. In determining whether malingering or related attributions has taken place, the assessor needs to consider the full examinee profile; PVT data speak only to invalid response set.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".