What are the best practices for psychotherapy with indigenous peoples in the United States and Canada? A thorny question.
Bibliographic record
Abstract
OBJECTIVE: This conceptual article addresses "best practices" for Indigenous Peoples in the United States and Canada. This topic is "thorny" both pragmatically (e.g., rare representation in clinical trials) and ethically (e.g., ongoing settler colonialism). METHOD: We outline four potential approaches, or "paths," in conceptualizing best practices for psychotherapy: (a) limiting psychotherapy to empirically supported treatments, (b) prioritizing the use of culturally adapted interventions, (c) focusing on common factors of psychotherapy, and (d) promoting grassroots Indigenous approaches and traditional healing. RESULTS: Lessons from our four-path journey include (a) the limits of empirically supported treatments, which are inadequate in number and scope when it comes to Indigenous clients, (b) the value of prioritizing interventions that are culturally adapted and/or evaluated for use with Indigenous populations, (c) the importance of common factors of evidence-based practice, alongside the danger of psychotherapy as a covert assimilative enterprise, and (d) the need to support traditional and grassroots cultural interventions that promote "culture-as-treatment." CONCLUSIONS: A greater commitment to community-engaged research and cultural humility is necessary to promote Indigenous mental health, including greater attention to supporting traditional healing and Indigenous-led cultural interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.042 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".