Substance Use Research with Indigenous Communities: Exploring and Extending Foundational Principles of Community Psychology
Bibliographic record
Abstract
Many Indigenous communities are concerned with substance use (SU) problems and eager to advance effective solutions for their prevention and treatment. Yet these communities also are concerned about the perpetuation of colonizing, disorder-focused, stigmatizing approaches to mental health, and social narratives related to SU problems. Foundational principles of community psychology-ecological perspectives, empowerment, sociocultural competence, community inclusion and partnership, and reflective practice-provide useful frameworks for informing ethical community-based research pertaining to SU problems conducted with and by Indigenous communities. These principles are explored and extended for Indigenous community contexts through themes generated from seven collaborative studies focused on understanding, preventing, and treating SU problems. These studies are generated from research teams working with Indigenous communities across the United States and Canada-inclusive of urban, rural, and reservation/reserve populations as well as adult and youth participants. Shared themes indicate that Indigenous SU research reflects community psychology principles, as an outgrowth of research agendas and processes that are increasingly guided by Indigenous communities. At the same time, this research challenges these principles in important ways pertaining to Indigenous-settler relations and Indigenous-specific considerations. We discuss these challenges and recommend greater synergy between community psychology and Indigenous research.
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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 | 0.085 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".