A Two-Eyed Seeing Framework for Building Indigenous Health Courses in Pharmacy
Bibliographic record
Abstract
In response to the Truth and Reconciliation Commission’s Calls to Action #23 and #24, health programs across Canada, including pharmacy, are integrating course content related to Indigenous Health and cultural safety. However, there is a paucity of literature on how to respectfully Indigenize health curricula. The purpose of this project was to develop a framework of better practices for both mandatory lecture-based and elective community-based courses. A Two-Eyed Seeing Model, incorporating both Indigenous and Western methodologies, was utilized to ensure the framework created was Indigenous-driven, evidence-based, and reciprocal. This process involved respectful collaboration with Indigenous partners and employed two methods: 1) a comprehensive literature review on better practices around Indigenous health course design from health programs across Canada, the US, Australia, and New Zealand, and 2) engagement with an Indigenous Advisory Committee (IAC) for their unique perspectives on key standards for course design. Based on the literature review and in-depth discussions with the IAC, 5 key pillars were identified for the framework: 1) Develop Indigenous community partnerships centered on mutual respect and trust, 2) Build learning objectives to increase student capacity to work effectively with Indigenous peoples, 3) Maintain community-university relationships by prioritizing reciprocity, 4) Align course activities with Indigenous pedagogies of teaching and learning, and 5) Pilot innovative assessment models for cultural safety learning. This framework acts as the basis of Indigenous-driven and evidence-based curriculum design and can be used as a guide for health programs across Canada in building Indigenous health courses.
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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 | 0.055 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".