Rethinking Nursing Best Practices with Aboriginal Communities: Informing Dialogue and Action
Bibliographic record
Abstract
This paper stems from findings of a literature review and consultation with key informants to explore nursing best practices in public health with rural and isolated Aboriginal communities. It summarizes background information on population distribution, the impact of colonization on Aboriginal health and the potential benefits for nurses and communities in adopting a partnership approach, rather than risking cultural imposition while applying best practices and knowledge derived from the dominant culture. The authors provide an alternative working definition for best practices in the context of public health nursing with Aboriginal communities based on findings from the literature review and key informant consultations. Findings include three principles for the development and assessment of nursing best practices with isolated Aboriginal communities: use of indigenous frameworks, capacity building and cultural safety. The discussion highlights examples that demonstrate the feasibility and strengths of these three principles across a selection of isolated, rural and national settings. Implications include a call for nursing leaders, managers and policy makers to take up this challenge and support wider dialogue and action to enable nursing practice that supports the efforts of Aboriginal people to improve health and social conditions.
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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 | 0.160 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.023 | 0.039 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".