Critical Incidents in Sustaining School-Wide Positive Behavioral Interventions and Supports
Bibliographic record
Abstract
The purpose of this study was to identify, categorize, and describe practitioners’ perspectives regarding factors that help and hinder sustainability of Tier I (universal) systems within School-Wide Positive Behavioral Interventions and Supports (SWPBIS). Seventeen participants involved in sustaining Tier I SWPBIS over several years within a school district were interviewed and asked what events affected its long-term implementation through a qualitative approach called the Critical Incident Technique (CIT). A total of 227 critical incidents were recorded and sorted into emergent unitary clusters based on content analysis. These categories then underwent rigorous reliability and validity checks, including expert analysis, inter-rater agreement, and participant feedback. This process yielded 13 categories that represent the participants’ experience of sustainability: Continuous Teaching, Positive Reinforcement, SWPBIS Team Effectiveness, Staff Ownership, School Administrator Involvement, Adaptation, Community of Practice, Use of Data, Involving New Personnel, Access to External Expertise, Maintaining Priority, Staff Turnover, and Conflict of Personal Beliefs/Mistaken Beliefs.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".