Boredom proneness and inattention in children with and without ADHD: the mediating role of delay aversion
Bibliographic record
Abstract
Objective: A high tendency to boredom and delay aversion are commonly observed traits among children and adolescents diagnosed with ADHD. However, the interplay between boredom, delay aversion, and ADHD symptoms remains unclear. It is unknown, for example, whether a predisposition to boredom predicts difficulties in sustaining attention because of susceptibility to delay aversion. This study investigated the potential mediating role of delay aversion in the relationship between boredom and inattentive behaviors in children with and without ADHD. Methods: Parent- and self-ratings of boredom proneness, delay aversion, and inattention symptom severity of 93 children with ADHD and 90 typically developing controls (aged 9-16 years) were included in analysis. Results: Both parent- and self-ratings showed that children with ADHD had significantly elevated levels of trait boredom, delay aversion, and inattention compared to controls. Trait boredom, delay aversion, and inattention were inter-correlated. The parent-ratings showed that children with a higher level of trait boredom tended to be more inattentive, and this effect was partially explained by the mediating role of delay aversion. Conclusions: Our findings suggest that the relation between boredom and inattention may be at least partially explained by the mediating role of delay aversion in children within and without 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".