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Record W2066168996 · doi:10.1097/ans.0000000000000070

Toward Cultural Safety

2015· article· en· W2066168996 on OpenAlexaff
Bernie Pauly, Jane McCall, Annette J. Browne, Joanne Parker, Ashley Mollison

Bibliographic record

VenueAdvances in Nursing Science · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsHarm reductionHarmNursingHealth careStigma (botany)FeelingQualitative researchSubstance useMedicineAddictionSocial stigmaPsychologyPsychiatryPublic healthSocial psychologyFamily medicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

As a group, people who use illicit drugs and are affected by social disadvantages often experience health inequities and encounter barriers such as stigma and discrimination when accessing health care services. Cultural safety has been proposed as one approach to address health inequities and mitigate stigma in health care. Drawing on a qualitative ethnographic approach within an overarching collaborative framework, we sought to gain an understanding of what constitutes culturally safe care for people who use(d) illicit drugs. The findings illustrate that illicit substance use in hospitals is often negatively constructed as (1) an individual failing, (2) a criminal activity, and (3) a disease of "addiction" with negative impacts on access to care, management of pain, and provision of harm-reduction supplies and services. These constructions of illicit substance use impact patients' feelings of safety in hospital and nurses' capacity to provide culturally safe care. On the basis of these findings, we provide recommendations and guidance for the development of culturally safe nursing practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0190.048
Scholarly communication0.0170.012
Open science0.0020.035
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0060.001

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 head0.048
GPT teacher head0.438
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations132
Published2015
Admission routes1
Has abstractyes

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