Psychometrically Informed Approach to Integration of Multiple Informant Ratings in Adult ADHD in a Community-Recruited Sample
Bibliographic record
Abstract
Although Diagnostic and Statistical Manual of Mental Disorders-Fifth edition requires that attention-deficit/hyperactivity disorder (ADHD) symptoms are apparent across settings, assessed by multiple informants, there remains no standardized approach to integration of multiple sources in adult ADHD diagnosis. The goal of the study was to evaluate informant effects on adult ADHD symptom ratings. Participants were 406 adults, ages 18 to 37, and identified second reporters, recruited from the community, and completing a comprehensive diagnostic and cognitive assessment, including a clinician-administered diagnostic interview and self- and other-report questionnaires of ADHD symptoms. Structural equation modeling indicated good fit for a trifactor model of ADHD, including general ADHD, specific inattention and hyperactivity-impulsivity, and self- and other-perspective factors. Yet there were a number of symptoms on the specific hyperactive-impulsive and self-factors that exhibited nonsignificant loadings. Significant differential item functioning across self-ratings and informant ratings was also noted. The external validation indices of laboratory executive function and diagnostic team-rated impairment was significantly correlated with the specific inattentive factor. While executive function was marginally significantly correlated with the other perspective factor, impairment was associated with the self-perspective factor. Overall, inattentive symptoms may be more sensitive measures of adult ADHD, and other and self-ratings may provide different information in relation to external criteria.
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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 | 0.046 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".