Optimal Timing for Launching Installation of Tiers 2 and 3 Systems of School-Wide Positive Behavioral Interventions and Supports
Bibliographic record
Abstract
The purpose of this longitudinal study was to examine patterns in implementation of Tiers 2 and 3 school-wide positive behavioral interventions and supports (SWPBIS) systems to identify timings of installation that led to higher implementation of advanced tiers. Extant data from 776 schools in 27 states reporting on the first 3 years of Tier 2 implementation and 359 schools in 23 states reporting on the first year of Tier 3 implementation were analyzed. Using structural equation modeling, we found that higher Tier 1 implementation predicted subsequent Tier 2 and Tier 3 implementation. In addition, waiting 2 or 3 years after initial Tier 1 implementation to launch Tier 2 systems predicted higher initial Tier 2 implementation (compared with implementing the next year). Finally, we found that launching Tier 3 systems after Tier 2 systems, compared with launching both tiers simultaneously, predicted higher Tier 2 implementation in the second and third year, so long as Tier 3 systems were launched within 3 years of Tier 2 systems. These findings provide empirical guidance for when to launch Tiers 2 and 3 systems; however, we emphasize that delays in launching advanced systems should not equate to delays in more intensive supports for students.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".