Coming together for something good: recommendations from a scoping review for dissemination and implementation science to improve indigenous substance use disorder treatment
Bibliographic record
Abstract
Introduction: Dissemination and Implementation (D&I) science is growing among Indigenous communities. Indigenous communities are adapting and implementing evidence-based treatments for substance use disorders (SUD) to fit the needs of their communities. D&I science offers frameworks, models, and theories to increase implementation success, but research is needed to center Indigenous knowledge, enhancing D&I so that it is more applicable within Indigenous contexts. In this scoping review, we examined the current state of D&I science for SUD interventions among Indigenous communities and identified best-practice SUD implementation approaches. Methods: PubMed and PsycINFO databases were queried for articles written in English, published in the United States, Canada, Australia, and New Zealand. We included key search terms for Indigenous populations and 35 content keywords. We categorized the data using the adapted and extended Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework that emphasizes equity and sustainability. RE-AIM has also been used as a primary model to consistently identify implementation outcomes. Results: = 4). Findings also highlighted the importance of the application of each RE-AIM domain for meaningful, well-defined community-engaged approaches. Conclusion: Finding indicated a need to prioritize Indigenous methods to culturally center, re-align and adapt Western treatments and frameworks to increase health equity and improve SUD treatment outcomes. Utility in the use of the modified RE-AIM and the continued modification for Indigenous communities was also noted.
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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 | 0.365 | 0.524 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.035 | 0.034 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.025 | 0.045 |
| Open science | 0.013 | 0.017 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".