Cultural interventions to treat addictions in Indigenous populations: findings from a scoping study
Bibliographic record
Abstract
BACKGROUND: Cultural interventions offer the hope and promise of healing from addictions for Indigenous people.a However, there are few published studies specifically examining the type and impact of these interventions. Positioned within the Honouring Our Strengths: Culture as Intervention project, a scoping study was conducted to describe what is known about the characteristics of culture-based programs and to examine the outcomes collected and effects of these interventions on wellness. METHODS: This review followed established methods for scoping studies, including a final stage of consultation with stakeholders. The data search and extraction were also guided by the "PICO" (Patient/population, Intervention, Comparison, and Outcome) method, for which we defined each element, but did not require direct comparisons between treatment and control groups. Twelve databases from the scientific literature and 13 databases from the grey literature were searched up to October 26, 2012. RESULTS: The search strategy yielded 4,518 articles. Nineteen studies were included from the United States (58%) and Canada (42%), that involved residential programs (58%), and all (100%) integrated Western and culture-based treatment services. Seventeen types of cultural interventions were found, with sweat lodge ceremonies the most commonly (68%) enacted. Study samples ranged from 11 to 2,685 clients. Just over half of studies involved quasi-experimental designs (53%). Most articles (90%) measured physical wellness, with fewer (37%) examining spiritual health. Results show benefits in all areas of wellness, particularly by reducing or eliminating substance use problems in 74% of studies. CONCLUSIONS: Evidence from this scoping study suggests that the culture-based interventions used in addictions treatment for Indigenous people are beneficial to help improve client functioning in all areas of wellness. There is a need for well-designed studies to address the question of best relational or contextual fit of cultural practices given a particular place, time, and population group. Addiction researchers and treatment providers are encouraged to work together to make further inroads into expanding the study of culture-based interventions from multiple perspectives and locations.
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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 | 0.037 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".