Validation of a Measure of District Systems Implementation of Positive Behavioral Interventions and Supports
Bibliographic record
Abstract
District leadership teams perform key roles in building the systems to support schools in the implementation of Positive Behavioral Interventions and Supports (PBIS). However, there is a lack of measures for assessment and progress monitoring specific to district PBIS systems. To address this gap, we evaluated the validity of a measure of implementation of district PBIS systems, the District Systems Fidelity Inventory (DSFI). Using 183 school districts and 760 schools implementing PBIS, we found the DSFI to have good evidence of structural validity for measuring nine aspects of district systems (Leadership Teaming, Stakeholder Engagement, Funding and Alignment, Policy, Workforce Capacity, Training, Coaching, Evaluation, and Local Implementation Demonstrations). We also found DSFI subscales to be moderately related to school-level PBIS implementation fidelity, providing evidence of convergent validity. We describe how leadership teams can use the DSFI to improve PBIS implementation and student outcomes.
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".