Leveraging community-based mental health services to reduce inequities for children and families living in United States who have experienced migration-related trauma.
Bibliographic record
Abstract
OBJECTIVE: Trauma systems therapy for refugees (TST-R) is a trauma-focused, culturally responsive mental health prevention and intervention model designed to meet the needs of children and families who are fleeing their home countries and seeking humanitarian refuge. TST-R provides trauma-focused mental health treatment and addresses problems in part exacerbated by harsh U.S. immigration policies (e.g., poor mental health, stigma, fear) that have implications for the psychosocial well-being of immigrant children and families, especially those who have experienced migration-related trauma. METHOD: Informed by a community-based participatory research approach, TST-R was developed as an adaptation of trauma systems therapy to address common barriers to care experienced by those of refugee and immigrant backgrounds, including mental health stigma, distrust of service systems, and cultural and linguistic barriers. RESULTS: TST-R is a multitiered and phase-based intervention that strategically addresses stressors and needs across levels of the social ecology. Most TST-R services are delivered in easily accessible, nonstigmatizing settings (e.g., school) by a cultural broker and a clinician who work in partnership. TST-R has been disseminated and implemented with multiple cultural groups (e.g., Somali, Bhutanese) across the United States and Canada. CONCLUSIONS: Given the unique stressors, strengths, and needs of immigrant children and their families, mental health services must be equitable, community based, and sustainable. TST-R demonstrates promise as a prevention and intervention model especially for those experiencing immigration policy-related stressors and may serve as a guide for developing child mental health policies and immigration policies that promote mental well-being for immigrant families. (PsycInfo Database Record (c) 2024 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".