Identifying and Predicting Distinct Patterns of Implementation in a School-Wide Behavior Support Framework.
Bibliographic record
Abstract
The purpose of this study was to examine the extent to which distinct patterns of fidelity of implementation emerged for 5331 schools over a 5-year course of implementing school-wide positive behavioral interventions and supports (SWPBIS). We used latent class analysis to classify schools based on their likelihood of implementing SWPBIS with fidelity each year, then assessed school and district predictors of classifications. A four-class solution fit the model well, with two patterns of sustained implementation (Sustainers and Slow Starters) and two patterns of practice abandonment (Late Abandoners and Rapid Abandoners). Significant predictors of group membership included grade levels served, enrollment, proportion of schools implementing SWPBIS in the district (“critical mass”), and size of the implementation cohort (“community of practice”). Elementary schools, larger schools, schools in districts with more schools already implementing SWPBIS, and those starting within a larger initial district cohort were more likely to be in the sustaining classes. Results are discussed in terms of understanding patterns of implementation in schools to enhance sustained implementation of school practices.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".