Blending Aboriginal and Western healing methods to treat intergenerational trauma with substance use disorder in Aboriginal peoples who live in Northeastern Ontario, Canada
Bibliographic record
Abstract
As with many Indigenous groups around the world, Aboriginal communities in Canada face significant challenges with trauma and substance use. The complexity of symptoms that accompany intergenerational trauma and substance use disorders represents major challenges in the treatment of both disorders. There appears to be an underutilization of substance use and mental health services, substantial client dropout rates, and an increase in HIV infections in Aboriginal communities in Canada. The aim of this paper is to explore and evaluate current literature on how traditional Aboriginal healing methods and the Western treatment model "Seeking Safety" could be blended to help Aboriginal peoples heal from intergenerational trauma and substance use disorders. A literature search was conducted using the keywords: intergenerational trauma, historical trauma, Seeking Safety, substance use, Two-Eyed Seeing, Aboriginal spirituality, and Aboriginal traditional healing. Through a literature review of Indigenous knowledge, most Indigenous scholars proposed that the wellness of an Aboriginal community can only be adequately measured from within an Indigenous knowledge framework that is holistic, inclusive, and respectful of the balance between the spiritual, emotional, physical, and social realms of life. Their findings indicate that treatment interventions must honour the historical context and history of Indigenous peoples. Furthermore, there appears to be strong evidence that strengthening cultural identity, community integration, and political empowerment can enhance and improve mental health and substance use disorders in Aboriginal populations. In addition, Seeking Safety was highlighted as a well-studied model with most populations, resulting in healing. The provided recommendations seek to improve the treatment and healing of Aboriginal peoples presenting with intergenerational trauma and addiction. Other recommendations include the input of qualitative and quantitative research as well as studies encouraging Aboriginal peoples to explore treatments that could specifically enhance health in their respective communities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".