Grooved Pegboard adds incremental value over memory-apparent performance validity tests in predicting psychiatric symptom report
Bibliographic record
Abstract
The present study evaluated whether Grooved Pegboard (GPB), when used as a performance validity test (PVT), can incrementally predict psychiatric symptom report elevations beyond memory-apparent PVTs. Participants (N = 111) were military personnel and were predominantly White (84%), male (76%), with a mean age of 43 (SD = 12) and having on average 16 years of education (SD = 2). Individuals with disorders potentially compromising motor dexterity were excluded. Participants were administered GPB, three memory-apparent PVTs (Medical Symptom Validity Test, Non-Verbal Medical Symptom Validity Test, Reliable Digit Span), and a symptom validity test (Personality Assessment Inventory Negative Impression Management [NIM]). Results from the three memory-apparent PVTs were entered into a model for predicting NIM, where failure of two or more PVTs was categorized as evidence of non-credible responding. Hierarchical regression revealed that non-dominant hand GPB T-score incrementally predicted NIM beyond memory-apparent PVTs (F(2,108) = 16.30, p < .001; R2 change = .05, β = −0.24, p < .01). In a second hierarchical regression, GPB performance was dichotomized into pass or fail, using T-score cutoffs (≤29 for either hand, ≤31 for both). Non-dominant hand GPB again predicted NIM beyond memory-apparent PVTs (F(2,108) = 18.75, p <.001; R2 change = .08, β = −0.28, p < .001). Results indicated that noncredible/failing GPB performance adds incremental value over memory-apparent PVTs in predicting psychiatric symptom report.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".