Critical Features Predicting Sustained Implementation of School-Wide Positive Behavioral Interventions and Supports
Bibliographic record
Abstract
The current study explored the extent to which a common measure of perceived implementation of critical features of Positive Behavioral Interventions and Supports (PBIS) predicted fidelity of implementation 3 years later. Respondents included school personnel from 261 schools across the United States implementing PBIS. School teams completed the Positive Behavioral Interventions and Supports Self-Assessment Survey to self-assess fidelity of implementation in different PBIS settings (school-wide, nonclassroom, classroom, individual). These scores were then analyzed to assess whether certain items predicted the fidelity of PBIS implementation, as assessed through another fidelity of implementation measure, the School-Wide Benchmarks of Quality, 3 years later. Regression analyses indicated that self-reported fidelity of implementation of Classrooms Systems significantly predicted sustained implementation and student outcomes, as assessed through levels of Office Discipline Referrals. Within Classroom Systems, regular acknowledgment of expected behaviors, matching instruction to student ability, and access to additional support were the strongest predictors of sustained implementation. Results are discussed in terms of critical areas for focusing PBIS training to increase the likelihood of sustained implementation.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".