Comparability of Fidelity Measures for Assessing Tier 1 School-Wide Positive Behavioral Interventions and Supports
Bibliographic record
Abstract
Several reliable and valid fidelity surveys are commonly used to assess Tier 1 implementation in School-Wide Positive Behavioral Interventions and Supports (SWPBIS); however, differences across surveys complicate consequential decisions regarding school implementation status when multiple measures are compared. To address this concern, the current study (a) provides updated convergent validity estimates for five fidelity measures, (b) tests mean differences in scores and reports the percentages of schools meeting recommended implementation criteria by measure, and (c) investigates sensitivity of the measures to differences between schools at varied levels of implementation. Across most surveys, convergent validity estimates were moderate ( r = .59–.71), and mean differences were negligible for all surveys other than the School-Wide Evaluation Tool (SET), on which higher scores were more likely to be obtained. Despite higher average scores, the SET classified similar percentages of schools as adequately implementing compared with other measures with a 70% implementation criterion, but fewer schools when compared with measures with an 80% criterion. Compared with other measures, the PBIS Self-Assessment Survey (SAS) was more sensitive to differences among schools at higher levels of implementation. Implications for SWPBIS research and fidelity assessment are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.106 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".