Test–retest reliability and convergent validity of the Test of Nonverbal Intelligence-Fourth Edition in patients with schizophrenia.
Bibliographic record
Abstract
Abstract Background Fluid intelligence deficits affect executive functioning and social behaviors in patients with schizophrenia. To help clinicians manage fluid intelligence deficits, a psychometric 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 patient 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 | 0.005 | 0.016 |
| 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 | 0.001 |
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".