The classification accuracy of the recognition memory test–words as a performance validity test is affected by gender and education.
Bibliographic record
Abstract
OBJECTIVE: Previous research suggested that Recognition Memory Test-Words (RMT-W) scores may be confounded by gender and handedness. This study was designed to examine its classification accuracy as a performance validity test (PVT) and susceptibility to demographic characteristics. METHOD: = 12.9). The RMT-W's classification accuracy was computed against psychometrically operationalized criterion groups. RESULTS: Optimal RMT-W cutoffs (≤ 42 to ≤ 40) produced a good combination of sensitivity (.62-.70) and specificity (.90-.96), correctly classifying 85.6%-87.7% of the sample. Women scored 1.5 points higher. RMT-W scores were unrelated to handedness but were correlated with education. A linear relationship emerged between level of education and the cutoff needed to achieve ≥ .90 specificity: ≤ 43 for ≥ 13 years of education, ≤ 41 for 12 years of education, and ≤ 39 for ≤ 11 years of education. RMT-W ≤ 45 had .91 specificity in women with postsecondary education. CONCLUSIONS: Results suggest that overall, the RMT-W remains an effective free-standing PVT. Gender, age, and handedness in isolation had minimal impact on RMT-W scores. However, education had a clinically significant effect. The combined effect of gender and education produced a marked shift in classification accuracy. Systematic research is needed on the relationship between demographics and PVT outcomes to ensure that cutoffs have the same clinical interpretation regardless of patient variables. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".