Indigenous peoples and medications for opioid use disorders: A scoping review.
Bibliographic record
Abstract
OBJECTIVE: This scoping review was to synthesize the rapidly accelerating literature on Indigenous peoples and medications for opioid use disorder (MOUD) and to identify barriers to implementation and sustainability. The article also addressed outcomes, perspectives, and suggestions for implementing culturally adapted MOUD programs. METHOD: We conducted a scoping review of articles indexed in MEDLINE, APA PsycInfo, Scopus, and ERIC (through September 2024). Articles needed to include substantive information on an Indigenous population (in Canada, the United States, New Zealand, or Australia), include content on MOUD, and address the intersection of MOUD and Indigenous populations. Titles/abstracts were screened by two reviewers, followed by a full-text review and data extraction. RESULTS: = 22). Overall, Indigenous clients have a mixed degree of engagement, retention, and positive outcomes within methadone, buprenorphine, and injectable opioid agonist treatment programs. Promising findings emerged for MOUD programs targeting Indigenous youth and that incorporate comprehensive cultural and health frameworks. Across MOUD types, Indigenous clients had consistently lower rates of treatment access and retention than did non-Indigenous clients. CONCLUSIONS: The findings emphasize the importance of aligning MOUD programs with Indigenous cultural frameworks and involving Indigenous consultation at all stages. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".