Wabishki Bizhiko Skaanj: a learning pathway to foster better Indigenous cultural competence in Canadian health research
Bibliographic record
Abstract
OBJECTIVE: In Canada, Indigenous people experience racism across diverse settings, including within the health sector. This has negatively impacted both the quality of care that Indigenous people receive as well as how research related to Indigenous populations is conducted. Therefore, an Indigenous-led council at a kidney research network, in partnership with other key stakeholders, sought to create a learning pathway that aims to distill the racism that Indigenous people face, and build cultural competence, within the health sector. PARTICIPANTS: The learning pathway was designed for researchers, health care providers, patient partners and administrators. SETTING: Various components of the pathway are established trainings in healthcare and research settings at provincial and national levels. Provincially, some components are implemented in British Columbia, Alberta, Saskatchewan, Manitoba and Ontario. INTERVENTION: The pathway, called Wabishki Bizhiko Skaanj (meaning "White Horse" in Anishinaabemowin), involves six key steps: a culturally tailored blanket exercise that walks participants through the history of local Indigenous Nations/peoples; a more detailed online training program (San'yas); a series of webinars on Indigenous research ethics and protocols; an educational booklet about engaging Knowledge Keepers in research, as well as sharing details about their traditional knowledge and culture; two certification programs about Indigenous ownership of data; and a "book club," wherein the conversation of racism-and the goal for finding solutions-is continually discussed. OUTCOMES: Wabishki Bizhiko Skaanj is working to build cultural competence in the Canadian health sector. IMPLICATIONS: This learning pathway has the potential to address racial disparities across the country and improve health outcomes for Indigenous peoples.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".