Incorporating First Nations, Inuit and Métis Traditional Healing Spaces within a Hospital Context: A Place-Based Study of Three Unique Spaces within Canada’s Oldest and Largest Mental Health Hospital
Bibliographic record
Abstract
Globally and historically, Indigenous healthcare is efficacious, being rooted in Traditional Healing (TH) practices derived from cosmology and place-based knowledge and practiced on the land. Across Turtle Island, processes of environmental dispossession and colonial oppression have replaced TH practices with a colonial, hospital-based system found to cause added harm to Indigenous Peoples. Growing Indigenous health inequities are compounded by a mental health crisis, which begs reform of healthcare institutions. The implementation of Indigenous knowledge systems in hospital environments has been validated as a critical source of healing for Indigenous patients and communities, prompting many hospitals in Canada to create Traditional Healing Spaces (THSs). After ten years, however, there has been no evaluation of the effectiveness of THSs in Canadian hospitals in supporting healing among Indigenous Peoples. In this paper, our team describes THSs within the Center for Addiction and Mental Health (CAMH), Canada's oldest and largest mental health hospital. Analyses of 22 interviews with hospital staff and physicians describe CAMH's THSs, including what they look like, how they are used, and by whom. The results emphasize the importance of designating spaces with and for Indigenous patients, and they highlight the wholistic benefits of land-based treatment for both clients and staff alike. Transforming hospital spaces by implementing and valuing Indigenous knowledge sparks curiosity, increases education, affirms the efficacy of traditional healing treatments as a standard of care, and enhances the capacity of leaders to support reconciliation efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".