Culturally safe communication and the power of language in Arctic nursing
Bibliographic record
Abstract
Nursing education and healthcare in Nunavut and Greenland have been developed, and to a large degree governed, by Danish and Euro-Canadian norms, culture, and language. Teachers and healthcare professionals are mostly Danish-speaking Danes in Greenland and English-speaking Euro-Canadians from southern Canada in Nunavut. This is not trivial for Greenlandic and Canadian Inuit nursing students or nurses, or for Canadian and Greenlandic Inuit healthcare recipients, the majority of whom speak Greenlandic or Inuktitut as their mother tongue. Drawing primarily on data from interviews with Canadian and Greenlandic Inuit nurses and nursing students between 2007 and 2010, I discuss the ways in which language as habitus may work to support or impede culturally safe care, workplaces, and education. I argue that the double-cultured Greenlandic and Canadian Inuit nurses and nursing students are invaluable to Arctic healthcare systems as culturally safe healthcare providers and habitus brokers. Furthermore, healthcare professionals from outside Greenland and Nunavut can advantageously learn from their Greenlandic and Canadian Inuit counterparts.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".