The Impact of Training Indigenous Facilitators for a Two-Eyed Seeing Research Treatment Intervention for Intergenerational Trauma and Addiction
Bibliographic record
Abstract
Intergenerational trauma in Indigenous Peoples was not the result of a targeted event, but rather political and governmental policies inflicted upon entire generations. The resultant effects of these traumas and multiple losses include addiction, depression, anxiety, violence, self-destructive behaviors, and suicide, to name but a few. Traditional healers, Elders, and Indigenous facilitators agree that the reclamation of traditional healing practices combined with conventional interventions could be effective in addressing intergenerational trauma and substance use disorders. Recent research has shown that the blending of Indigenous traditional healing practices and the Western treatment model Seeking Safety resulted in a reduction of intergenerational trauma (IGT) symptoms and substance use disorders (SUD). This article focuses on the Indigenous facilitators who were recruited and trained to conduct the sharing circles as part of the research effort. We describe the six-day training, which focused on the implementation of the Indigenous Healing and Seeking Safety model, as well as the impact the training had on the facilitators. Through the viewpoints and voices of the facilitators, we explore the growth and changes the training brought about for them, as well as their perception of how their changes impacted their clients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".