Trauma-informed Care Interventions in Emergency Medicine:A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Trauma exposure is a highly prevalent experience for patients and clinicians in emergency medicine (EM). Trauma-informed care (TIC) is an effective framework to mitigate the negative health impacts of trauma. This systematic review synthesizes the range of TIC interventions in EM, with a focus on patient and clinician outcomes, and identifies gaps in the current research on implementing TIC. METHODS: The study was registered with PROSPERO (CRD42020205182). We systematically searched peer-reviewed journals and abstracts in the PubMed, EMBASE (Elsevier), PsycINFO (EBSCO), Social Services Abstract (ProQuest), and CINAHL (EBSCO) databases from 1990 onward on August 12, 2020. We analyzed studies describing explicit TIC interventions in the ED setting using inductive qualitative content analysis to identify recurrent themes and identify unique trauma-informed interventions in each study. Studies not explicitly citing TIC were excluded. Studies were assessed for bias using the Newcastle-Ottawa criteria and Critical Appraisal Skills Programme (CASP) Checklist. RESULTS: We identified a total of 1,372 studies and abstracts, with 10 meeting inclusion criteria for final analysis. Themes within TIC interventions that emerged included educational interventions, collaborations with allied health professionals and community organizations, and patient and clinician safety interventions. Educational interventions included lectures, online modules, and standardized patient exercises. Collaborations with community organizations focused on addressing social determinants of health. All interventions suggested a positive impact from TIC on either clinicians or patients, but outcomes data remain limited. CONCLUSION: Trauma-informed care is a nascent field in EM with limited operationalization of TIC approaches. Future studies with patient and clinician outcomes analyzing universal TIC precautions and systems-level interventions are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.000 |
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; both teacher heads agree on what is shown here.
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".