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Record W1999557791 · doi:10.1177/1744987114546724

Nursing in media-saturated societies: implications for cultural safety in nursing practice in Aotearoa New Zealand

2014· article· en· W1999557791 on OpenAlexaboutno aff
Raymond Nairn, Ruth DeSouza, Angela Barnes, Jenny Rankine, Belinda Borell, Tim McCreanor

Bibliographic record

VenueJournal of research in nursing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaIndigenousPublic relationsCultural safetyNursingSociologyEthnic groupPolitical scienceGender studiesHealth careMedicineLaw

Abstract

fetched live from OpenAlex

This educational piece seeks to apprise nurses and other health professionals of mass media news practices that distort social and health policy development. It focuses on two media discourses evident in White settler societies, primarily Australia, Canada, New Zealand and the United States, drawing out implications of these media practices for those committed to social justice and health equity. The first discourse masks the dominant culture, ensuring it is not readily recognised as a culture, naturalising the dominant values, practices and institutions, and rendering their cultural foundations invisible. The second discourse represents indigenous peoples and minority ethnic groups as ‘raced’ – portrayed in ways that marginalise their culture and disparage them as peoples. Grounded in media research from different societies, the paper focuses on the implications for New Zealand nurses and their ability to practise in a culturally safe manner as an exemplary case. It is imperative that these findings are elaborated for New Zealand and that nurses and other health professionals extend the work in relation to practice in their own society.

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)0.011
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.125
GPT teacher head0.538
Teacher spread0.413 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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