Effort-testing In Children Undergoing PsychoEducational Assessment Using The Medical Symptom Validity Test
Bibliographic record
Abstract
Suboptimal effort affects the reliability and validity of clinical data. Effort measures of a sample of French Canadian schoolchildren who had undergone a psycho-educational assessment were compared with eight other schoolchildren samples presumed to have exerted good effort, using the Medical Symptom Validity Test (MSVT) and the Reliable Digit Span (RDS). The study focused, more specifically, on the MSVT effort measures, Immediate Recognition (IR), Delayed Recognition (DR) and Consistency (CNS) because they are relatively insensitive to impairments. Overall, the majority of children, with and without impairment passed the MSVT effort measures with a fail rate of 13.1% for MSVT and 36.1% for RDS. The correlation between failure of each measure was only moderately significant. The failure rate on the MSVT dropped to 6.1% after removal of participants who read below a grade three level and the correlation between failure on the RDS and failure on MSVT became insignificant. This is consistent with the literature documenting the use of MSVT and WMT with adults, with and without impairments, and supports the assertion that insufficient effort is likely the only explanation for failure on MSVT when the participants have the required reading level Green suggested considering evidence of a Genuine Memory Impairment Profile (GMIP) in failing protocols (i.e., when the average of IR, DR, and CNS exceeds the average of PA and FR by at least 20 points). In this study, all but one child who failed MSVT demonstrated a GMIP profile.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".