Age-Related Variance in Performance versus Ratings of Attention and Impulse Regulation in Children: Implications for the Assessment of ADHD
Bibliographic record
Abstract
Executive function task (EF) deficits are hypothesized to underlie difficulties with self-regulation. However, tasks assessing EF impairments have only been weakly correlated with rating scales that index self-regulation difficulties. A community sample of children and youth aged between 8 and 20 years old were assessed longitudinally. Growth curve analyses and correlations were conducted to better understand how these two types of measures relate to one another across development, as well as the impact of age-related variance. EF was assessed using the Stroop Task and Trail Making test and behavioral ratings of self-regulation were captured using the SWAN scale. EF task performance improved steeply until age 14-15, whereas the SWAN Scale showed small age-related decreases. EF task performance was moderately correlated with age among 8-13-year-olds and to a lesser extent among 14-20-year-olds. SWAN scores were not significantly related to age in either group. Correlations were similar in an ADHD "at-risk" subgroup. EF task performance and parent ratings of attention regulation have different developmental trajectories, which may partly explain why correlations are low to modest in these samples. In particular, age-related variance is an important methodological consideration with significant implications for the assessment of self-regulation in children and youth 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".