Emotion dysregulation and ADHD subclinical manifestations in late adolescents: A study with a focus on inattention
Bibliographic record
Abstract
Recent influential approaches to this topic consider executive functions as a bridge between emotional dysregulation and hyperactivity/attention related disorders. Specifically, the ability to self-regulate emotions is viewed as a part of executive functions, which have a particular impact on attentional control. This study explored the relationships between self-reported attention disorders and emotional dysregulation in a sample of 132 non-clinical high school students (age: M = 18.6; SD = .71; 66% males). The research battery comprised four self-report measures which were individually administered to the participants: Brown Attention-Deficit Disorder Scales (Brown ADD Scales), Behavior Rating Inventory of Executive Function – Adult Version (BRIEF-A), Difficulties in Emotion Regulation Scale (DERS), Toronto Alexithymia Scale – 20 items (TAS). A series of regression analyses confirmed the stringent relation between the abilities to identify, regulate, and express emotions and the core variables involved in Attention Deficit Hyperactivity Disorder (ADHD), especially in relation to impairments in attentive functioning. Results throw light on the importance of emotion dysregulation in attention and executive control, suggesting the relevance of assessing the individual’s abilities to manage affects to better conceptualize the disorder and plan interventions. Implications for research and practice are discussed especially in the context of psychological development and protraction of the condition during adulthood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".