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Record W4411293776 · doi:10.12927/cjnl.2025.27608

Developing a Practice Standard to Address Indigenous-Specific Racism in Healthcare: A Case Study

2025· article· en· W4411293776 on OpenAlexvenueaboutno aff
Dave Bhauruth, Rebekah Smith, Adiam Brhane, Sabrina Luke

Bibliographic record

VenueNursing leadership · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRacismHealth careNursingSociologyNursing researchPsychologyPolitical scienceMedicineGender studiesLaw

Abstract

fetched live from OpenAlex

Racism in healthcare directed toward Indigenous Peoples in Canada is an ongoing issue resulting in significant disparities in morbidity and mortality. The British Columbia College of Nurses and Midwives (BCCNM) is the health profession regulatory college for all nursing designations and registered midwives. In 2021-2022, BCCNM developed and implemented the Indigenous Cultural Safety, Cultural Humility, and Anti-Racism practice standard, the first of its kind in Canada, which applies to all British Columbia nursing and midwifery registrants. This case study describes the policy development process and lessons learned for developing the Indigenous Cultural Safety, Cultural Humility, and Anti-Racism practice standard.

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)0.017
metaresearch head score (Gemma)0.026
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: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0160.006
Scholarly communication0.0040.003
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.360
GPT teacher head0.522
Teacher spread0.163 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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