Teacher Behaviors Toward Children with Attention-Deficit/Hyperactivity Disorder Predict Peers' Initial Liking and Disliking Impressions in a Summer Camp Setting
Bibliographic record
Abstract
Psychological studies traditionally focus on problem behaviors and clinical diagnoses of children to explain their liking and disliking by peers. Children with Attention-Deficit/Hyperactivity Disorder (ADHD), in particular, often display problem behaviors resulting in their social impairment. However, adults' behaviors toward a child are an understudied factor that may also affect the impressions that peers form about that child. Participants were 137 previously unacquainted children ages 6.8–9.8 (24 with ADHD and 113 typically developing children) in a 2-week summer day program, along with their camp teachers. Data were analyzed via longitudinal social network analyses that controlled for children's ADHD diagnostic status and disruptive and internalizing behaviors. Results suggested that camp teachers' observed highlighting of children's personal strengths predicted these children receiving more liking nominations from peers and, for children with ADHD, fewer disliking nominations over the course of camp. Camp teachers' highlighting of behavioral compliance was not associated with peers' impressions of children. Camp teachers' public correction of children predicted these children's receipt of fewer liking nominations, among children with low disruptive behavior. Camp teachers' discreet corrections did not show this effect. Specific adult behaviors toward children, when displayed in front of peers, may influence peers' liking and disliking impressions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".