Indigenous Nurse and White Settler Nurse Teaching Teams: Learning to Disrupt With Indigenist Nursing Education
Bibliographic record
Abstract
Purpose: Nurse educators are called on to confront the reality of systemic racism embedded in their own institutions as they educate students to promote equity through strength-based Indigenous-specific anti-racist practice. However, schools of nursing and nursing pedagogies centre whiteness. White settler nurse educators often lack the competence and confidence to teach an anti-racist and Indigenist curriculum. Indigenous nurse educators often bear a disproportionate responsibility for anti-racist and Indigenist curriculum while also being in the minority and lacking institutional support. Innovative and effective teaching strategies are required to address these issues. Method: Through a teaching and scholarship grant, Indigenous nurses working in the community and white settler nurse teaching teams were established. These teams, led by an Indigenous scholar, delivered an Indigenist curriculum focused on core concepts of decolonization, relationship, and obligation. Results: Educator feedback demonstrated how Indigenous and white settler collaboration in the classroom is a mutually beneficial yet challenging innovation. Conclusion: Indigenous community nurses and white settler nurse teaching teams can collaborate to effectively deliver an anti-racism curriculum, promoting confidence and competence.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".