A novel unstructured performance-based task of executive function in children with attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
OBJECTIVE: Executive functions (EFs) have been assessed with performance-based measures and rating scales. Research has shown a lack of association between these two methods. One factor that might contribute to this difference is the structure provided on performance-based measures that is not provided on rating scales. This study examined the role of structure on self-directed task completion, an aspect of EF, using a novel unstructured performance-based task (UPT). METHOD: Children aged 8-12 years (38 attention-deficit/hyperactivity disorder, ADHD; 42 typically developing) and their caregivers participated. We compared performance on the UPT, performance-based measures of EF (Stroop test and Trail-Making Test), and a rating scale to assess EF (Barkley Deficits in Executive Functioning Scale-Children and Adolescents, BDEFS-CA). RESULTS: Group differences were found across all measures. Significant associations emerged between the UPT and Stroop test, Trail-Making Test, and BDEFS-CA, but no significant associations were found between the Stroop test or Trail-Making Test and the BDEFS-CA. In regression analyses, performance-based tasks and the rating scale both uniquely predicted UPT performance. The UPT was a significant predictor of group status when entered with performance-based tasks, but the UPT did not enter as a significant predictor when entered with the rating scale. CONCLUSION: The UPT is a promising measure to assess self-directed task completion in children with 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".