Implementation and evaluation of a two-eyed seeing approach using traditional healing and seeking safety in an indigenous residential treatment program in Northern Ontario
Bibliographic record
Abstract
Indigenous clients in need of residential care for substance use disorders (SUD) often present with the diagnosis of substance use disorder (SUD) combined with intergenerational trauma (IGT) or both. SUD is exceedingly prevalent amongst Indigenous peoples due to the health impacts of colonisation, residential school trauma, and IGT on this population's health. We evaluated the effectiveness of a Two-Eyed Seeing approach in a four-week harm reduction residential treatment programme for clients with a history of SUD and IGT. This treatment approach blended Indigenous Healing practices with Seeking Safety based on Dr. Teresa Marsh's research work known as Indigenous Healing and Seeking Safety (IHSS). The data presented in this study was drawn from a larger trial. This qualitative study was undertaken in collaboration with the Benbowopka Treatment Centre in Blind River, Northern Ontario, Canada. Patient characteristic data were collected from records for 157 patients who had enrolled in the study from April 2018 to February 2020. Data was collected from the Client Quality Assurance Survey tool. We used the qualitative thematic analysis method to analyse participants' descriptive feedback about the study. Four themes were identified: (1) Motivation to attend treatment; (2) Understanding Benbowopka's treatment programme and needs to be met; (3) Satisfaction with all interventions; and (4) Moving forward. We utilised a conceptualised descriptive framework for the four core themes depicted in the medicine wheel. This qualitative study affirmed that cultural elements and the SS Western model were highly valued by all participants. The impact of the harm reduction approach, coupled with traditional healing methods, further enhanced the outcome. This study was registered with clinicaltrials.gov (identifier number NCT0464574).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".