Results From Three Performance Validity Tests in Children With Intellectual Disability
Bibliographic record
Abstract
If we wish to conclude that failure on a performance validity test (PVT) is a false positive for poor effort, we must have evidence that the person is truly incapable of passing the test because of cognitive impairment. We must show that they have a diagnostic condition that is sufficient to account fully for failure on that test. The purpose of the current study was to evaluate the performance of children with a primary diagnosis of intellectual disability (ID) on the Word Memory Test (WMT), the Medical Symptom Validity Test (MSVT), and the Nonverbal Medical Symptom Validity Test (Green, 2003 Green, P. (2003). Green's Computerized Word Memory Test for Windows. User's manual. Edmonton, AB, Canada: Green's Publishing. [Google Scholar], 2004 Green, P. (2004). Green's Medical Symptom Validity Test: User's manual. Edmonton, AB, Canada: Green's Publishing. [Google Scholar], 2008b Green, P. (2008b). Green's Nonverbal Medical Symptom Validity Test: User's manual. Edmonton, AB, Canada: Green's Publishing. [Google Scholar]; Green & Astner, 1995 Green, P., & Astner, K. (1995). Oral Word Memory Test: User's manual. Raleigh, NC: Cognisyst. [Google Scholar]). If a Full-Scale IQ (FSIQ) less than 70 could account for failure on these tests in adults, then children with ID would also fail them. In fact, the children with ID in the current study did not fail the WMT or MSVT as long as they had at least a Grade 3 reading level. Also, the children with ID did not fail these tests any more often than did children of significantly higher intelligence. The data suggest that an FSIQ in the range of 48 to 70 is not sufficient to explain failure on these PVTs by children or adults.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".