A critical review of empirical support for trauma‐informed approaches in schools and a call for conceptual, empirical and practice integration
Bibliographic record
Abstract
Abstract Interest in trauma‐informed approaches in schools is high throughout the US, UK, Australia, Canada and other countries, but the empirical evidence on whole‐school responses to trauma is limited. This conceptual and theoretical review explores relevant literature; outlines existing conceptual models for trauma‐informed organisations, including schools; reviews current evidence for individual components of conceptual models relevant to schools; and considers implications for future research, practice and policy. Four common components were identified in the literature: (a) understanding trauma and making a universal commitment to address it; (b) emphasising physical, emotional and psychological safety for all school members; (c) taking a strengths‐based, whole‐person approach toward staff, students and families; and (d) creating and sustaining trusting, collaborative and empowering relationships among all school constituents. Most of these components have been studied as part of other literature and are not specific to trauma‐informed schools. Practitioners would benefit from shifting to an organisational model for trauma rather than the historical emphasis on interpersonal approaches, toward ensuring that all staff members are trauma‐aware and ‐responsive, and emphasising the creation of healthy, healing schools for all communities.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".