Time-dependent changes in positively biased self-perceptions of children with attention-deficit/hyperactivity disorder: A developmental psychopathology perspective
Bibliographic record
Abstract
This study examined changes in the degree of positive bias in self-perceptions of previously diagnosed 8- to 13-year-old children with attention-deficit/hyperactivity disorder (ADHD; n = 513) and comparison peers (n = 284) over a 6-year period. The dynamic association between biased self-perceptions and dimensional indices of depressive symptoms and aggression also were considered. Across the 6-year time span, comparison children exhibited less bias than children with ADHD, although a normative bolstering of social self-views during early adolescence was observed. Decreases in positive biases regarding social and behavioral competence were associated with increases in depressive symptoms over time, whereas increases in levels of positively biased self-perceptions in the behavioral (but not social) domain were predictive of greater aggression over time. ADHD status moderated the dynamic association between biases and adjustment. Finally, evidence indicated that there was a bidirectional relationship between biases and aggression, whereas depressive symptoms appeared to inversely predict later bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".