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Record W2637500070 · doi:10.7202/1040146ar

Culturally safe communication and the power of language in Arctic nursing

2017· article· en· W2637500070 on OpenAlexafffundvenueabout
Helle Møller

Bibliographic record

VenueÉtudes/Inuit/Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsLakehead University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsDanishHealth careNursingArcticFirst languagePower (physics)Health professionalsMedicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.498
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes4
Has abstractyes

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