Creating space for Indigenous healing practices in patient care plans
Bibliographic record
Abstract
BACKGROUND: The Truth and Reconciliation Commission of Canada's Calls to Action ask that those who can effect change within the Canadian healthcare system recognize the value of Indigenous healing practices and support them in the treatment of Indigenous patients. METHODS: We distributed a survey to the Canadian Rheumatology Association membership to assess awareness of Indigenous healing practices, and attitudes informing their acceptance in patient care plans. RESULTS: We received responses from 77/514 members (15%), with most (73%) being unclear or unaware of what Indigenous healing practices were. Nearly all (93%) expressed interest in the concept of creating space for Indigenous healing practices in rheumatology care plans. The majority of support was for the use in preventive or symptom management strategies, and less as adjuncts to disease activity control. Themes identified through qualitative analysis of free-text responses included a desire for patient-centered care and support for reconciliation in medicine, but with a colonial construct of medicine, demonstration of an evidence bias, and hierarchy of medicines. CONCLUSIONS: Overall, respondents were open to the idea of inclusion of Indigenous healing practices in patient's car plans, emphasizing importance for patient empowerment and patient-centered care. However, they cited concerns that provide the indication for further learning and reconciliation in medicine.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".