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Record W4412572717 · doi:10.1177/10436596251353518

Cultural Safety Knowledge and Practices Among Internationally Qualified Nurses Caring for Indigenous Peoples in Australia, New Zealand and Canada: A Scoping Review

2025· review· en· W4412572717 on OpenAlexaboutno aff
Pratibha Bhandari, Ling Zeng, Anne‐Marie Eades, Danielle Manton, Annie Hepworth, Carolyn Antoniou, Elaine Correia Moll, Jack Cornish, Suzanne Sheppard‐Law

Bibliographic record

VenueJournal of Transcultural Nursing · 2025
Typereview
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
FundersUniversity of Technology Sydney
KeywordsCultural safetyIndigenousInclusion (mineral)NursingTraditional knowledgeCulturally appropriateGrey literatureBest practiceHealth careCultural competenceMedicineScarcityMEDLINEPublic relationsPolitical sciencePsychologyFamily medicineSociologySocial sciencePedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: Culturally safe practices are crucial for equitable health care for Indigenous Peoples. Despite the vital role of internationally qualified nurses in delivering patient care in the host countries, there is limited evidence on their knowledge and practices of cultural safety. This paper, aims to identify and map existing evidence on cultural safety knowledge and practices among internationally qualified nurses in Australia, New Zealand and Canada. METHOD: A scoping review was conducted using a comprehensive search strategy across five electronic databases and gray literature. RESULTS: Three studies met the inclusion criteria. Findings were grouped into two categories: knowledge on cultural safety and challenges in translating this knowledge into practice. DISCUSSION: Our review highlights the scarcity of evidence in this area. The findings from the limited existing evidence underscore the urgent need to plan future research on knowledge and practices related to cultural safety among internationally qualified nurses to promote health outcomes for Indigenous Peoples.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.106
GPT teacher head0.481
Teacher spread0.375 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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