Teacher attributions for children's attention‐deficit/hyperactivity disorder behaviors predict experiences with children and with classroom behavioral management in a summer program practicum
Bibliographic record
Abstract
Abstract Elementary school teachers often implement classroom behavioral management systems to address student misbehavior. Common problems targeted by these systems are the inattentive, hyperactive, and impulsive behaviors characteristic of attention‐deficit/hyperactivity disorder (ADHD). This study examined teachers' attributions for why children display ADHD behaviors, and how such attributions affect their experiences with children in the context of interventions to manage these behaviors. Participants were 32 preservice teachers undertaking a practicum in a summer program for 137 children (Grades 1–3), some of whom had ADHD. Teachers were trained to implement classroom‐wide behavioral management. Teachers' attributions for children's ADHD behaviors were assessed using a vignette measure, before teachers had met their students or begun training on intervention techniques. When controlling for attributions regarding oppositional behavior, teachers' initial attributions for ADHD behaviors as less internal/controllable predicted children reporting more positive relationships with that teacher during the summer program. Teachers' initial attributions for ADHD behaviors as less stable predicted teachers' greater satisfaction with the intervention techniques during the summer program and their greater attunement to children's social networks. Cognitions about the causes of children's ADHD behaviors held by preservice teachers may relate to their subsequent experiences with children in the context of implementing classroom behavioral management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".