Trauma-informed system-level policies in healthcare settings: A scoping review
Bibliographic record
Abstract
Trauma-informed care (TIC) is widely recognised as essential for improving health outcomes and reducing retraumatisation. While most studies have focussed on clinical implementation, embedding TIC principles at the system and policy levels is increasingly considered necessary for sustainable, equitable change in healthcare. To map the existing literature on system-level trauma-informed policies in healthcare settings and identify key domains, implementation strategies and reported outcomes. This scoping review followed Arksey and O’Malley’s six-stage framework, with enhancements from Levac et al. , and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A systematic search was conducted across the PubMed, MEDLINE, CINAHL, PsycINFO and Scopus databases for studies published between January 2000 and June 2025. Eligible studies described trauma-informed organisational or system-level policies in healthcare and included empirical data, frameworks and implementation reports. Twelve studies focussing on mental health and primary care, mainly from the United States and Canada, were included. Five recurring domains have emerged: workforce development and training, physical and environmental safety, leadership and governance commitment, patient and staff empowerment and language and procedural changes. Implementation strategies included staff education, advisory board involvement, policy redesign and leadership engagement. Outcomes were inconsistently reported but included improvements in staff attitudes, patient satisfaction and a reduction in coercive practices. Trauma-informed system-level policies hold significant promise for promoting equity, safety and cultural transformation in healthcare. However, current evidence remains limited and concentrated in specific settings. Future research should prioritise evaluation, diverse context applications and co-leadership with people with lived experience to advance trauma-informed system change.
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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 | 0.036 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".