Urban Land-Based Healing: A Northern Intervention Strategy
Bibliographic record
Abstract
Urban Indigenous populations face significant health and social disparities across Canada. With high rates of homelessness and substance use, there are often few options for urban Indigenous Peoples to access land-based healing programs despite the increasingly known and appreciated benefits. In May 2018, the first urban land-based healing camp opened in Yellowknife, Northwest Territories, Canada, one of the first to our knowledge in Canada or the United States. This camp may serve as a potential model for an Indigenous-led and Indigenous-based healing camp in an urban setting. We present preliminary outcome data from the healing camp in a setting with a high-risk population struggling with substance use and homelessness. Reflections are presented for challenging logistical and methodological considerations for applications elsewhere. This northern effort affords us ample opportunity for expanding the existing knowledge base for land- based healing applied to an urban Indigenous high-risk setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".