Nursing the Nuu-chah-nulth Way: Communities Driving Nursing Policy Priorities
Bibliographic record
Abstract
Rural and remote Indigenous communities face unique challenges, and they must drive solutions for sustaining and maintaining distinct nursing practices. Resourcing Indigenous community needs and aspirations for health depends on sustainable funding and an appropriately resourced nursing workforce. An Indigenous community-engaged research team led a program of study exploring Indigenous systems of care with three distinct communities. We used Indigenous research methodologies to identify obstacles to care and ways to advance nursing and healthcare delivery according to unique values and demographical and geographical influences. Using a collaborative analysis approach with communities, we identified themes related to resourcing nursing positions, supporting nursing education and valuing nursing influence in determining program priorities. The voice of the community in research is a powerful force for advocacy, ensuring that nurses are supported in relationships with communities and in designing programs that fit the community's vision for health and wellness. We recognize the essential contributions of nurse leaders to policy processes in formulating and coordinating ideas for program redesign across and within levels of organizations for health and social justice impacts. We conclude our paper by noting implications for nursing leadership in diverse settings with the goal of sustaining a nursing workforce to provide culturally safe, wellness-focused care.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".