Preliminary development and validation of the Interconnected Systems Framework-Implementation Inventory (ISF-II).
Bibliographic record
Abstract
As schools increasingly implement multitiered systems of support, there is a pressing need to develop psychometrically sound implementation fidelity measures. The interconnected systems framework (ISF) is a multitiered model blending systems of positive behavioral interventions and supports with promotion, prevention, and intervention strategies of school mental health. The ISF is being implemented in communities across the United States with ongoing evaluation in several randomized controlled trials. The ISF-Implementation Inventory (ISF-II) was developed to measure fidelity of the ISF within a school building. We conducted a national validation study including completion of the ISF-II by 398 educators in 49 schools, 16 school districts, and 9 states. Results indicate the ISF-II produces scores that are internally consistent and structurally valid when items are organized into a three-tiered model. Additionally, the ISF-II was rated as feasible, acceptable, and beneficial. Limitations of the study, including the need for additional psychometric testing, are discussed in light of these results that suggest educators and researchers, alike, should feel confident in using the ISF-II as a measure of ISF implementation quality. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.040 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".