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Record W4410363079 · doi:10.4103/cjrm.cjrm_15_24

‘We’re racially profiled as drunk Indians’ – experiences of Indigenous rural British Columbians accessing health care

2025· article· en· W4410363079 on OpenAlexaffvenueabout
Terri Aldred, Erika Pritchard, Jordan Christmas, James Liu, Dana Hubler

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsPositive Living NorthProvincial Health Services AuthorityUniversity of British Columbia
Fundersnot available
KeywordsIndigenousRacismThematic analysisGeneral partnershipHealth careFocus groupSociologyNorthern territoryGender studiesNursingQualitative researchPolitical scienceMedicineEthnologyAnthropology

Abstract

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INTRODUCTION: The present study examines the experiences of anti-Indigenous racism derived from the data collected across rural British Columbia (BC) through the Rural Coordination Centre of British Columbia's Rural Site Visits Project. This study aims to demonstrate how and where people are being treated inequitably in the healthcare system and to highlight racism and its negative impact on patient care. METHODS: We used an action-oriented methodology and incorporated Indigenous methods into the analysis. Participants and focus groups were identified using Boelen's Partnership Pentagon Model, and the data were collected using an appreciative inquiry approach. The data were analysed using thematic analysis. RESULTS: The various forms of racism appearing in health care were identified together with their impacts on patient care. Primary care providers appeared unaware of the extent of discrimination and barriers to care affecting Indigenous community members, suggesting a disconnect between provider perception and patient experience. Finally, the steps towards culturally safe health care were proposed. CONCLUSIONS: Anti-Indigenous racism exists and adversely impacts the care Indigenous peoples receive throughout rural BC. Our study invites healthcare providers to reflect upon their practice and become more culturally aware and humble to improve Indigenous peoples' access to care, health outcomes and experiences. INTRODUCTION: Notre article examine les expériences de racisme anti-autochtone tirées des données recueillies dans les zones rurales de la Colombie-Britannique dans le cadre du Rural Coordination Centre of British Columbia's Rural Site Visits Project (projet de visites des sites ruraux). Ce document vise à démontrer comment et où les personnes sont traitées de manière inéquitable dans le système de santé et à mettre en évidence le racisme et son impact négatif sur les soins aux patients. MTHODES: Nous avons utilisé une méthodologie orientée vers l'action et intégré des méthodes autochtones dans l'analyse. Les participants et les groupes de discussion ont été identifiés à l'aide du modèle Pentagone du partenariat de Boelen et les données ont été recueillies à l'aide d'une approche d'enquête appréciative. Les données ont été analysées à l'aide d'une analyse thématique. RSULTATS: Les différentes formes de racisme apparaissant dans les soins de santé ont été identifiées, ainsi que leur impact sur les soins aux patients. Les prestataires de soins primaires ne semblaient pas conscients de l'ampleur de la discrimination et des obstacles aux soins qui affectaient les membres de la communauté autochtone, ce qui suggère un décalage entre la perception du prestataire et l'expérience du patient. Enfin, les étapes des soins de santé culturellement sûrs sont proposés. CONCLUSIONS: Le racisme anti-autochtone existe et a un impact négatif sur les soins que reçoivent les populations autochtones dans les zones rurales de la Colombie-Britannique. Notre article invite les prestataires de soins de santé à réfléchir à leur pratique et à devenir plus conscients de la culture et plus humbles afin d'améliorer l'accès aux soins, les résultats en matière de santé et les expériences des populations autochtones.

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 score0.144
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.323
Teacher spread0.312 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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