The hierarchical factor model of ADHD: invariant across age and national groupings?
Bibliographic record
Abstract
OBJECTIVE: To examine the factor structure of attention-deficit/hyperactivity disorder (ADHD) in a clinical sample of 1,373 children and adolescents with ADHD and their 1,772 unselected siblings recruited from different countries across a large age range. Hierarchical and correlated factor analytic models were compared separately in the ADHD and sibling samples, across three different instruments and across parent and teacher informants. Specific consideration was given to factorial invariance analyses across different ages and different countries in the ADHD sample. METHOD: A sample of children and adolescents between 5 and 17 years of age with ADHD and their unselected siblings was assessed. Participants were recruited from seven European countries and Israel. ADHD symptom data came from a clinical interview with parents Parental Account of Childhood Symptoms and questionnaires from parents and teachers (Conners Parent and Teacher). RESULTS: A hierarchical general factor model with two specific factors best represented the structure of ADHD in both the ADHD and unselected sibling groups, and across informants and instruments. The model was robust and invariant with regard to age differences in the ADHD sample. The model was not strongly invariant across different national groups in the ADHD sample, likely reflecting severity differences across the different centers and not any substantial difference in the clinical presentation of ADHD. CONCLUSIONS: The results replicate previous studies of a model with a unitary ADHD component and separable specific traits of inattention and hyperactivity/impulsivity. The unique contribution of this study was finding support for this model across a large developmental and multinational/multicultural sample and its invariance across ages.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".