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Record W2127096545 · doi:10.12927/cjnl.2010.21595

Rethinking Nursing Best Practices with Aboriginal Communities: Informing Dialogue and Action

2010· article· en· W2127096545 on OpenAlexaffvenue
Dawn Smith, Nancy Edwards, Wendy E. Peterson, Maria Jaglarz, Dorothy Laplante, Alma Estable

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIndigenousBest practiceNursingGeneral partnershipPublic relationsContext (archaeology)Action (physics)SociologyNursing literaturePolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.629
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.229
GPT teacher head0.406
Teacher spread0.177 · 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

Citations14
Published2010
Admission routes2
Has abstractyes

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