Trauma and Trauma-Informed Care in Early Intervention in Psychosis: State of Implementation and Provider Views on Challenges
Bibliographic record
Abstract
OBJECTIVE: Although trauma is increasingly recognized as a major risk factor for psychosis and for its link to treatment outcomes, the landscape of trauma-related practices in specialized early psychosis services in the United States and other countries remains only poorly characterized. Research documenting the perspectives of frontline providers is also lacking. The primary goals of this study were to document the state of trauma-related policy implementation in early intervention in psychosis (EIP) programs and to gather provider perspectives. METHODS: This was a mixed-methods project involving an international EIP provider survey, followed by in-depth provider interviews. The survey was disseminated in Australia, Canada, Chile, the United Kingdom, and the United States. In total, 164 providers, representing 110 unique sites, completed the survey. Frequencies were calculated for responses to survey items, and open-ended responses were analyzed with a systematic content analysis. RESULTS: The survey findings suggested low implementation rates for a variety of assessment and support practices related to trauma and trauma-informed care. Coding of open-ended responses revealed numerous concerns and uncertainties among providers regarding the relationship between trauma and psychosis and the state of the EIP field. CONCLUSIONS: An expansion of research and service development aimed at better meeting the trauma-related needs of young people with psychosis is essential, with implications for EIP outcomes and service user and staff experiences.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".