Mâmawoh Kamâtowin, "Coming together to help each other in wellness": Honouring Indigenous Nursing Knowledge
Bibliographic record
Abstract
This paper is the result of coming to know and better understand Indigenous nursing experience in First Nations, Inuit and Métis communities. Using an Indigenous research approach, I (first author) drew from the collective experience of four Indigenous nurse scholars and attended to the question of how Indigenous knowledge manifests itself in the practices of Indigenous nurses and how it can better serve individuals, families, and communities. This research framework centered on Indigenous principles, processes, and practical values as expressed in Indigenous nursing practice. The results were woven from key understandings and meanings of Indigeneity as a way of being. Central to this study was that Indigenous knowledge has always been fundamental to the ways that these Indigenous nurses have undertaken nursing practice, regardless of the systemic and historical barriers they faced in providing healthcare for Indigenous people. The results of this research demonstrated how Indigenous nurses consistently drew on their inherited Indigenous knowledge to deliver nursing care to Indigenous people. Their identity as Indigenous persons was integral to their identities as Indigenous nurses. Of significance is the personal and particular description of how these Indigenous nurse scholars developed their nursing approaches in relevance to how health and healthcare delivery must be integrated into healthcare systems as a pathway to reducing health disparities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".