A Systematic Review of Trauma Interventions in Native Communities
Bibliographic record
Abstract
American Indian/Alaska Native and First Nations communities suffer from health disparities associated with multiple forms of trauma exposure. Culturally appropriate interventions are needed to heal current and historical trauma wounds. Although there are evidence-based trauma interventions for other populations, few have been implemented or evaluated with Native communities. Understanding the extant research on trauma interventions in Native communities is crucial for advancing science and filling gaps in the evidence base, and for meeting the needs of underserved people. In this systematic review of the literature on trauma interventions in Native communities in the United States, Canada, Australia, and New Zealand, we identified 15 studies representing 10 interventions for historical and/or current trauma. These studies involved the community to some extent in developing or culturally adapting the interventions and suggested positive outcomes with regard to historical and interpersonal trauma symptoms. However, notable limitations in study design and research methods limit both internal validity and external validity of these conclusions. Only one study attempted (but did not achieve) a quasi-experimental design, and small sample sizes were persistent limitations across studies. Recommendations for researchers include working in partnership with Native communities to overcome barriers to trauma intervention research and to increase the rigor of the studies so that ongoing efforts to treat trauma can yield publishable data and communities can secure funding for intervention research.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".