Decolonizing mental health practice through traditional healing frameworks: Insights from Canada, China, Singapore, and the United States.
Bibliographic record
Abstract
Decolonial and liberation psychology aims to understand and address the social and epistemic injustices in our mental health systems, practices, and research agenda. To advance this goal, we advocate for deeper engagement with traditional healing systems practiced by various Indigenous Peoples and cultural groups around the world. In this article, we consider examples of Indigenous healing from Canada, China, Singapore, and the United States, to address a central question: What can we learn from these unique Indigenous healing traditions to inform mental health practices globally? Comparison shows that all these practices involve communal healing rituals grounded in spiritual, religious, and cultural knowledge systems related to embodied ways of knowing and that are embedded in social-ecological systems, including kinship, ancestral ties, and filial connections to the cosmology. To support further development of decolonial practice, it is crucial to attend to the complex interactions of cultural identity and sociocultural (relational, communal, political, and spiritual) factors underlying healing traditions in Indigenous communities. (PsycInfo Database Record (c) 2025 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".