Addressing experiences of trauma within Indigenous-focused substance use residential treatment: a systematic review and environmental scan
Bibliographic record
Abstract
Indigenous individuals in Canada disproportionally experience higher rates of substance use concerns. This study examined clinical practices currently implemented with Indigenous-led residential treatment facilities to simultaneously address substance use and post-traumatic stress. A systematic review of relevant literature retrieved published approaches to address these concurrent disorders with Indigenous individuals. This review retrieved 35 sources related to trauma and substance use treatment among Indigenous individuals or communities. Among these sources, all leveraged cultural approaches as a dual treatment for trauma symptoms and substance use. Inconsistent results were reported among those sources (n = 3) who analyzed comparisons with wait-list controls or used randomized-controlled designs. Using culture-as-treatment was elaborated upon in the second goal of this study: an environmental scan of Indigenous-led treatment programs and qualitative interviews with 10 treatment center staff to understand how programs may address both substance use and traumatic symptoms among Indigenous-led substance use treatment centers across Canada. When we searched the websites of these centers, we found that approximately 38% (16 of 43) of treatment centers discussed implementing some form of treatment that addressed trauma symptoms in conjunction with primary substance use. Among the 10 staff participants, all discussed how trauma can impede client success in treatment, and ACE-specific programming is useful within their respective treatment programs. Results showed that when manualized treatments are used, they must be adapted to meet the specific needs of Indigenous communities, and culture-as-treatment is a popular approach among Indigenous-led treatment centers, particularly for addressing trauma symptoms.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".