A path towards relational accountability in British Columbia's health system: Grounding systems transformation in Coast Salish Teachings and Indigenous-specific anti-racism
Bibliographic record
Abstract
Ongoing settler-colonialism within British Columbia's (BC's) healthcare system excludes First Nations Knowledge systems, perpetuating significant harm and inequities for Indigenous Peoples. Health systems transformation requires centring First Nations land-based laws and Teachings alongside Indigenous-specific anti-racism. The Provincial Health Services Authority's (PHSA's) journey of accepting Coast Salish Teachings gifted by Coast Salish Knowledge Keeper Te Ta-in provides a pathway on embodying relational accountability and distinctions-based approaches. The Teachings inspire people to grow and serve in new ways, embracing the truth of Indigenous-specific racism, incorporating lived experiences of Indigenous Peoples, and doing our best as human beings. Grounded in local First Nations Knowledge and Indigenous thought leadership, PHSA’s approach demonstrates how land-based laws and Indigenous-specific anti-racism praxis can drive transformation to create an anti-racist, culturally safe, and equitable health system in line with the BC Declaration Act on the Rights of Indigenous Peoples , the In Plain Sight Report, provincial commitments, and legislative obligations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.046 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| 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".