Primary and secondary prevention of behavior difficulties: Developing a data‐informed problem‐solving model to guide decision making at a school‐wide level
Bibliographic record
Abstract
Abstract This article focuses on the development and implementation of primary and secondary behavior supports at a schoolwide level. The approach described is consistent with previous efforts to address behavior at a systems level (e.g., G. Sugai, R.H. Horner, & F.M. Gresham, 2002). In this article, we illustrate this process through a school‐based example. This example is drawn from a larger project in which area regional school‐district consultants and university researchers partnered with four elementary schools in an effort to enhance each school's capacity to implement evidence‐based practice and decisions at primary (i.e., universal or school‐wide), secondary (i.e., targeted efforts for selected groups of students and/or settings), and tertiary (i.e., individual‐student) levels to promote behavioral competence. The project incorporated promising strategies and tools designed to promote and sustain the use of evidence‐based practices and data‐driven problem solving. Continuous progress monitoring of systemic variables and student behavioral outcomes (e.g., office‐referral data) helped to guide systemic reform efforts. Reductions were noted in the number of student discipline problems, and improvements were noted in critical features of school‐wide effective behavior support at a systems level. Results are discussed with an emphasis on implications for practice, lessons learned from this project, and directions for additional research. © 2007 Wiley Periodicals, Inc. Psychol Schs 44: 7–18, 2007.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".