Development and validation of the Trauma Responsive Schools Implementation Assessment (TRS-IA).
Bibliographic record
Abstract
OBJECTIVE: School systems are increasingly interested in becoming trauma-responsive settings. While many school and district teams seek guidance and tools to assess and improve their trauma-responsive efforts, to our knowledge, there are no systematically developed tools to help teams plan and evaluate this work. This article describes the development and validation of the Trauma Responsive Schools Implementation Assessment (TRS-IA). METHOD: Study 1 describes measure development using a modified version of the RAND Corporation/University of California, Los Angeles Appropriateness Method, a systematic approach that involves a literature review and multistage expert input. Study 2 describes the validation of the TRS-IA with a national sample of 2,694 school and district personnel. RESULTS: Study 1 findings resulted in seven domains and 32 indicators for the TRS-IA. Study 2 supported the TRS-IA as an empirically validated, reliable measure to assess schools' implementation of trauma responsiveness. CONCLUSION: Our results suggest that the TRS-IA may be a pragmatic tool to guide the implementation and evaluation of trauma-responsive school system efforts. (PsycInfo Database Record (c) 2025 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 | 0.058 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".