Engaging Indigenous community to improve healthcare environments: Is reconciliation within hospital spaces possible?
Bibliographic record
Abstract
This research employs a community-engaged methodology with the Centre for Addictions and Mental Health (CAMH) to examine 1) What are Indigenous [and non-Indigenous] peoples’ relationships to traditional healing spaces within hospital settings; and, 2) What do these spaces mean for health and healing among those who access these spaces? This topic bears important scholarly and policy relevance. Not only do Indigenous people suffer the poorest health outcomes in Canada, they also experience ongoing institutional and structural trauma, much of which is experienced in hospital environments. Indigenous realities of racism and differential access to care in Canadian healthcare institutions are unparalleled, such that hospitals have been described as unsafe spaces for Indigenous people.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".