Implementing Trauma-Informed Care—Settings, Definitions, Interventions, Measures, and Implementation across Settings: A Scoping Review
Bibliographic record
Abstract
Traumatic experiences can have long-lasting negative effects on individuals, organizations, and societies. If trauma is not addressed, it can create unsafe cultures with constant arousal, untrusting relationships, and the use of coercive measures. Trauma-informed care (TIC) can play a central role in mitigating these negative consequences, but it is unknown how and in which way(s) TIC should be implemented. Our objective was to conduct a scoping review that systematically explored and mapped research conducted in this area and to identify existing knowledge about the implementation of TIC. The search was conducted on the CINAHL, Cochrane, Embase, ERIC, Medline, PsycINFO, and Web of Science databases, and more than 3000 empirical papers, published between 2000 and 2022, were identified. Following further screening, we included 157 papers in our review, which were mainly from the USA, Australia, New Zealand, and Canada, focusing on study settings, methodologies, and definitions of TIC, as well as the types of interventions and measures used. This review shows that TIC is a complex and multifaceted framework, with no overarching structure or clear theoretical underpinnings that can guide practical implementations. TIC has been defined and adapted in varied ways across different settings and populations, making it difficult to synthesize knowledge. A higher level of agreement on how to operationalize and implement TIC in international research could be important in order to better examine its impact and broaden the approach.
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.
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 | 0.054 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| 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".