Test–retest reliability and convergent validity of the test of nonverbal intelligence-fourth edition in patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Fluid intelligence deficits affect executive functioning and social behaviors in patients with schizophrenia. To help clinicians manage fluid intelligence deficits, a psychometrically sound measure is needed. The purposes of this study were to examine the test-retest reliability and convergent validity of the Test of Nonverbal Intelligence-Fourth Edition (TONI-4) assessing fluid intelligence in patients with schizophrenia. METHODS: A total of 103 patients with stable condition were assessed with the TONI-4 twice with a 4-week interval to examine the test-retest reliability. We further used the Montreal Cognitive Assessment (MoCA) and the Tablet-Based Symbol Digit Modalities Test (T-SDMT) to examine the convergent validity of the TONI-4. RESULTS: The intra-class correlation coefficient was 0.73 for the TONI-4. The percentages of standard error of measurement and minimal detectable change for the TONI-4 were 5.1 and 14.2%, respectively. The practice effect of the TONI-4 was small (Cohen's d = - 0.03). Convergent validity showed small to moderate significant correlations between the TONI-4 and the MoCA as well as the T-SDMT (r = 0.35, p = .011 with the T-SDMT and r = 0.61, p < .001 with the MoCA). The results demonstrated that the TONI-4 had good test-retest reliability, limited random measurement error, and a trivial practice effect. The convergent validity of the TONI-4 was good. CONCLUSIONS: These findings indicate that the TONI-4 has potential to be a reliable and valid assessment of fluid intelligence in patients with schizophrenia.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".