Emotion knowledge, emotion regulation, and psychosocial adjustment in children with nonverbal learning disabilities
Bibliographic record
Abstract
Nonverbal learning disability is a childhood disorder with basic neuropsychological deficits in visuospatial processing and psychomotor coordination, and secondary impairments in academic and social-emotional functioning. This study examines emotion recognition, understanding, and regulation in a clinic-referred group of young children with nonverbal learning disabilities (NLD). These processes have been shown to be related to social competence and psychological adjustment in typically developing (TD) children. Psychosocial adjustment and social skills are also examined for this young group, and for a clinic-referred group of older children with NLD. The young children with NLD scored lower than the TD comparison group on tasks assessing recognition of happy and sad facial expressions and tasks assessing understanding of how emotions work. Children with NLD were also rated as having less adaptive regulation of their emotions. For both young and older children with NLD, internalizing and externalizing problem scales were rated higher than for the TD comparison groups, and the means of the internalizing, attention, and social problem scales were found to fall within clinically concerning ranges. Measures of attention and nonverbal intelligence did not account for the relationship between NLD and Social Problems. Social skills and NLD membership share mostly overlapping variance in accounting for internalizing problems across the sample. The results are discussed within a framework wherein social cognitive deficits, including emotion processes, have a negative impact on social competence, leading to clinically concerning levels of depression and withdrawal in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".