Reshaping Policies to Achieve a Strategic Plan for Indigenous Engagement in Nursing Education
Bibliographic record
Abstract
Canadian universities are developing strategies to address the Truth and Reconciliation Commission (TRC) Calls to Action. There has been much attention paid to the positivist, individualistic and Eurocentric foundations of nursing and its educational curricula, but limited focus on assessing organizational structures or engaging with stakeholders. Without both approaches, the success of new initiatives may be limited. The College of Nursing at the University of Saskatchewan implemented a "Learn Where You Live" model that demonstrated a sense of place by providing access and opportunity in rural, remote and northern regions of the province. Key to this initiative was the creation of the position of Strategist for Outreach and Indigenous Engagement, whose role it is to develop strategic initiatives designed to interpret and influence socio-political and policy-level system changes. This paper shares how adding a political scientist to nursing education created an interprofessional team by introducing new ways of thinking and being that have cultural relevance and understanding for a sustainable future.
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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 | 0.045 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| 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".