A review of evidence based practices to support students with oppositional defiant disorder in classroom settings
Bibliographic record
Abstract
The purpose of this systemic review of empirical research was to investigate available evidence-based interventions for use with students with oppositional defiant disorder (ODD) in general classroom settings. ODD is a specific disorder characterised by angry/irritable mood, argumentative/defiant behavior, and vindictiveness. Often ODD is hidden in the extant literature, as it is categorized under the umbrella term emotional and behavioral disorders (EBD) along with sometimes non-related disorders (attention-disorders, mood disorders, anxious disorders). This review of 26 articles focused on interventions for students whose behaviours were characteristic of ODD in classroom settings. While much of the research regarding the treatment of ODD consists of clinical strategies (e.g., family therapy, exercise programs, and community supports), it is essential that teachers have strategies to support students with ODD in inclusive general education classroom settings. Three main types of interventions emerged from this review: functional behaviour analysis, group contingency, and self-monitoring strategies. A number of other non-categorical strategies are also presented and discussed. Percentages of nonoverlapping data (PND) were calculated to explore the effect of these interventions in improving adaptive behavior, and in decreasing disruptive behavior. The resulting review provides recommendations and strategies for how teachers can support students with ODD in their classrooms.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".