“The Old Ways Are the Old Ways”: A Qualitative Study of Chinese Medicine Care in Indigenous Communities in Canada
Bibliographic record
Abstract
Objectives: Owing to colonization's impacts, Indigenous Peoples in Canada face a disproportionate share of health challenges and suffer inequitable access to health care today. In recent years, an increasing number of Indigenous-led health services have emerged, which—informed by decolonial principles, including “culture-as-cure”—holistically center local Indigenous cultural, spiritual, and healing knowledges and practices. Aligned with decolonial principles, this work examines the delivery of Chinese Medicine (CM) care—an East Asian Indigenous therapeutic approach—in Indigenous communities in British Columbia, Canada. Design: Informed by qualitative interviews with three licensed CM practitioners and one biomedical clinician working in such clinics, the work provides a descriptive account of clinical operations, and thematically explores operational successes and challenges. Results: Four CM clinical programs were identified, all operating on First Nations reserves, including settings at multidisciplinary community health centers, a First Nation Band Council office, and a school gymnasium. Most CM care was delivered free of charge, funded variously by nonprofit agency donations and provincial government reimbursement. Three central themes emerged across the study interviews. The first, transculturalism, emphasizes the conceptual overlap between CM and Indigenous belief systems in the Canadian context, which participants described as a source of strength in building trust for CM care as a nonlocal Indigenous therapeutic approach. The second theme, Cultural Humility, characterizes non-Indigenous practitioners' respectful outlook as guests on Indigenous land, taking community members' lead as to how they might best serve. The final theme, Multidimensional Healing, explores the physical, mental, and emotional healing that practitioners witnessed across their work. Conclusions: Despite economic and logistical challenges, study respondents expressed optimism about the potential for similar traditional medicine clinics to provide culturally resonant primary care in other underserved communities. Further research to learn about the experiences of First Nations community members receiving CM care is warranted.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.043 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".