A scoping study of cultural interventions to treat addictions in Indigenous populations: methods, strategies and insights from a Two-Eyed Seeing approach
Bibliographic record
Abstract
BACKGROUND: This paper describes the methods, strategies and insights gained from a scoping study using a "Two-Eyed Seeing" approach. An evolving technique, Two-Eyed Seeing respects and integrates the strengths of Indigenous knowledge and Western sciences, often "weaving back and forth" between the two worldviews. The scoping study was used to inform a tool for measuring the impact of culturally based addictions treatment services on wellness in Indigenous populations. It formed part of a three-year study, Honouring Our Strengths: Indigenous Culture as Intervention in Addictions Treatment. The scoping study identified and mapped literature on cultural interventions in addictions treatment, and described the nature, extent and gaps in literature. METHODS: Using a Two-Eyed Seeing approach, we adapted, applied and enhanced a common framework of scoping studies. In the end stage of the scoping review process, an Ad Hoc Review Group, led by our project Elder, reviewed and interpreted Indigenous and Western understandings within the mapped information. Elements of the scoping study were joined with results from community focus groups with staff at treatment centres. RESULTS: Two-Eyed Seeing contributed differently at each stage of the scoping study. In early stages, it clarified team expertise and potential contributions. At the mid-point, it influenced our shift from a systematic to a scoping review. Near the end, it incorporated Western and Indigenous knowledge to interpret and synthesize evidence from multiple sources. CONCLUSIONS: This paper adds to the collective work on augmenting the methodology of scoping studies. Despite the challenges of a Two-Eyed Seeing approach, it enables researchers using scoping studies to develop knowledge that is better able to translate into meaningful findings for Indigenous communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.235 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".