Applying a Trauma Informed School Systems Approach: Examples from School Community-Academic Partnerships
Bibliographic record
Abstract
Objectives: Schools can play an important role in addressing the effects of traumatic stress on students by providing prevention, early intervention, and intensive treatment for children exposed to trauma. This article aims to describe key domains for implementing trauma-informed practices in schools. Design: The Substance Abuse and Mental Health Administration (SAMHSA) has identified trauma-informed domains and principles for use across systems of care. This article applies these domains to schools and presents a model for a Trauma-Informed School System that highlights broad macro level factors, school-wide components, and tiered supports. Community partners from one school district apply this framework through case vignettes. Results: Case 1 describes the macro level components of this framework and the leveraging of school policies and financing to sustain trauma-informed practices in a public health model. Case 2 illustrates a school founded on trauma-informed principles and practices, and its promotion of a safe school environment through restorative practices. Case 3 discusses the role of school leadership in engaging and empowering families, communities, and school staff to address neighborhood and school violence. Conclusions: This article concludes with recommendations for dissemination of trauma-informed practices across schools at all stages of readiness. We identify three main areas for facilitating the use of this framework: 1) assessment of school staff knowledge and awareness of trauma; 2) assessment of school and/or district's current implementation of trauma-informed principles and practices; 3) development and use of technology-assisted tools for broad dissemination of practices, data and evaluation, and workforce training of clinical and non-clinical staff.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".