A Novel Look at Peer Problems: Examining Predictors of Children’s Sociometric Ratings of Classmates With ADHD Symptoms
Bibliographic record
Abstract
Research predominantly focuses on problematic behaviors in children with symptoms of attention-deficit/hyperactivity disorder (ADHD) to explain why they are disliked by their classroom peers. By contrast, the current study explores characteristics of peers that are associated with them disliking classmates with ADHD symptoms. To do so, we undertook a novel methodological approach using hierarchical linear modeling to examine the strength of the association between child characteristics, their sociometric ratings given to classmates, and the recipients’ ADHD symptom levels. Participants were 194 children (Grades K–4) in 12 classrooms. Using the sociometric method, children rated their liking versus disliking of each classmate. Children’s ADHD symptoms were reported by the teacher. Children’s self-reported stigma about ADHD, their own sociometric ratings received, and teacher ratings of children’s academic competence were collected. Results suggested that children who reported more stigma about ADHD, and who were more socially and academically competent, had a stronger negative association between the sociometric ratings they gave and the recipients’ ADHD symptoms (i.e., were more likely to dislike classmates with ADHD symptoms). These effects were strongest at the end of the academic year relative to the beginning of the year. Implications for interventions targeting the peer group 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".