Indigenous Healing and Seeking Safety: A Blended Implementation Project for Intergenerational Trauma and Substance Use Disorders
Bibliographic record
Abstract
Background: As with many Indigenous groups around the world, Aboriginal communities in Canada face significant challenges with trauma and substance use disorders (SUD). Treatment for intergenerational trauma (IGT) and SUD is challenging due to the complexity of both disorders. There is strong evidence that strengthening cultural identity, incorporating traditional healing practices, encouraging community integration, and inviting political empowerment can enhance and improve mental health and substance use disorders in Aboriginal populations. Methods: The purpose of this study was to explore whether the blending of Indigenous traditional healing practices and the Western treatment model Seeking Safety, which is used to treat post-traumatic stress disorder (PTSD) and SUD, resulted in a reduction of IGT symptoms and SUD. Twelve Aboriginal men and 12 Aboriginal women were recruited into this study—all of whom resided in Northern Ontario and self-identified as having experienced IGT and SUD. The Indigenous Healing and Seeking Safety (IHSS) group (conducted as sharing circles) were offered twice a week over 13 weeks. Data was collected via semi-structured interviews as well as an end-of-treatment focus group. A qualitative thematic analysis was performed to depict themes. Results: Out of the 24 Aboriginal people who entered the program, nine women and eight men completed the program. Analysis from the qualitative thematic data identified four core themes. Furthermore, the sharing circles and the presence of Elders and Aboriginal helpers increased the benefits of the blended approach. Conclusion: Evidence from this qualitative study suggests that it could be beneficial to incorporate Indigenous traditional healing practices into Seeking Safety to enhance the health and well-being of Aboriginal people with IGT and SUD. This implementation project, if replicated, has the potential to enhance the health and well-being of Aboriginal peoples. The use of blended implementation can be an important option for clinicians and health-care professionals when working with Aboriginal populations. Only the qualitative results will be discussed in this article; quantitative results will be published separately.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".