Practitioner Review: Do performance‐based measures and ratings of executive function assess the same construct?
Bibliographic record
Abstract
BACKGROUND: Both performance-based and rating measures are commonly used to index executive function in clinical and neuropsychological assessments. They are intended to index the same broad underlying mental construct of executive function. The association between these two types of measures was investigated in the current article. METHOD AND RESULTS: We examined the association between performance-based and rating measures of executive function in 20 studies. These studies included 13 child and 7 adult samples, which were derived from 7 clinical, 2 nonclinical, and 11 combined clinical and nonclinical samples. Only 68 (24%) of the 286 relevant correlations reported in these studies were statistically significant, and the overall median correlation was only .19. CONCLUSIONS: It was concluded that performance-based and rating measures of executive function assess different underlying mental constructs. We discuss how these two types of measures appear to capture different levels of cognition, namely, the efficiency of cognitive abilities and success in goal pursuit. Clinical implications of using performance-based and rating measures of executive function are discussed, including the use of these measures in assessing ADHD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".