A systematic review of trauma intervention adaptations for indigenous caregivers and children: Insights and implications for reciprocal collaboration.
Bibliographic record
Abstract
OBJECTIVE: Indigenous peoples are at elevated risk of exposure to trauma and related mental and physical health difficulties that are rooted in the ongoing experience of settler-colonialism. Historical and current trauma exposure feed intergenerational cycles that compromise the healthy development of Indigenous children. METHOD: We conducted a systematic review of trauma-focused, caregiver-child interventions adapted for Indigenous communities. RESULTS: We identified 13 articles each reporting a unique intervention. Six were implemented among American Indians, five among Indigenous Australians, one among First Nations and Metis peoples, and one among Māori peoples. Eight of the interventions used surface-structure cultural adaptations (i.e., replacing images or examples for greater cultural relevance), one used deep-structure cultural adaptations (i.e., replacing curriculum for greater cultural relevance), and four were culturally grounded interventions (i.e., developed by the Indigenous community in partnership with researchers). CONCLUSIONS: The overall limited number of trauma-focused, caregiver-child interventions for Indigenous communities, and especially those representing reciprocal collaboration between researchers and the communities with whom they engage, is notable. We argue that such collaboration is critical to healing Indigenous traumatization from colonization and provide recommendations for future trauma intervention science. (PsycInfo Database Record (c) 2022 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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".