A “two-eyed seeing” approach to Indigenizing nursing curricula
Bibliographic record
Abstract
Educational institutions, including schools of nursing, find themselves in significant times, as they work to Indigenize programs, and strive to repair and heal relationships with Indigenous peoples as recommended in the Truth and Reconciliation Commission of Canada (2015). Educators question where to begin the process, how such Indigenization should occur, and what the curricular end result should look like. In response, the authors considered many aspects from the literature, specific to nursing programs. The following themes were explored: partnering with community, cultural relevance, and faculty development. Through the utilization of a “two-eyed seeing” approach, institutional administrators need to partner with Indigenous Elders and community members to facilitate relationships required to provide the knowledge necessary to bring about change within educational programs. It is through such an approach that nursing curricula can be designed to be culturally safe and relevant for both Indigenous and non-Indigenous learners, and faculty can be supported in their growth and development in Indigenous knowledge. The authors propose that through “two-eyed seeing” and the integration of the Aboriginal Nurses Association of Canada (2009) core competencies, Indigenization of nursing curricula may ultimately move forward in a culturally reciprocal and respectful way.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".