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Record W1530545607 · doi:10.1111/phn.12210

The Need for Cultural Safety in Injury Prevention

2015· review· en· W1530545607 on OpenAlexaff
Audrey R. Giles, Sarah Hognestad, Lauren Brooks

Bibliographic record

VenuePublic Health Nursing · 2015
Typereview
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychological interventionCultural safetyPublic healthIntervention (counseling)MedicineCultural competencePoison controlCultural sensitivityOccupational safety and healthCultural issuesInjury preventionNursingCultural diversityPublic relationsPsychologySociologyHealth careMedical emergencyPolitical sciencePedagogyPsychotherapist

Abstract

fetched live from OpenAlex

Public health nurses are on the front line of injury prevention initiatives. However, within injury prevention interventions and research, issues pertaining to culture are often addressed through the employment of one of the three approaches: cultural competency, cultural appropriateness, and/or cultural sensitivity. When using these approaches, it is often suggested that it is only those who are the recipients of an intervention or the focus of research that "have" culture. The injury prevention designer's/provider's/researcher's own culture, as well as the ways in which it may influence the interventions or research, is typically rendered invisible. In this paper, we provide an overview and illustrations of the use of cultural competency, cultural appropriateness, and cultural sensitivity in injury prevention initiatives, as well as each approach's shortcomings. We then introduce cultural safety, an approach that has not yet gained traction in injury prevention but has had significant uptake within nursing in general, and argue that it has the potential to overcome many other approaches' shortcomings and thus may lead to more effective and socially just injury prevention initiatives.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.295
GPT teacher head0.543
Teacher spread0.248 · 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 designNot applicable
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

Citations12
Published2015
Admission routes1
Has abstractyes

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