Performance Calibration in Children With Attention-Deficit/Hyperactivity Disorder (ADHD) And A Community Sample of Typically Developing Children
Bibliographic record
Abstract
This study investigated performance calibration in 81 8-12 year-old children [M = 119.89 months (or 9 years, 11months), SD = 14.92 months, 26 females], 39 with and 42 without ADHD, from the Greater Toronto Area. Performance calibration was evaluated across the domains of general knowledge, emotion recognition, and recognition of the valence of social situations using several indices. Parents of children in the clinical ADHD group rated their childrens performance calibration skills significantly lower than parents in the typically developing (TD) group. While no group difference in performance calibration for general knowledge items were observed, the easy-hard effect was demonstrated, such that harder items elicited overconfidence and easier items elicited underconfidence across groups. The clinical ADHD group rated their effort expenditure significantly lower across most tasks although there were no differences in the perception of difficulty compared to the TD group. The clinical ADHD group was significantly overconfident on emotion recognition compared to the TD group, whereas the TD group was more accurate and underconfident across emotions. The clinical ADHD group was more confident overall when recognizing the valence of ambiguous social situations and more confident on the negative situations compared to the TD group. Thus, this study obtained performance calibration differences across facial emotion recognition and interpretation of social situations for children with ADHD compared to a TD group. The Bias Index, a measure of under/overconfidence, was recommended for future examinations of group differences in performance calibration. An investigation of the clinical utility of different performance calibration measures is presented and implications for interventions are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".