Primary and secondary prevention of behavior difficulties: Developing a data‐informed problem‐solving model to guide decision making at a school‐wide level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it