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Record W4313580030 · doi:10.5204/ijcis.2614

Acknowledging colonialism in the room: Barriers to culturally safe care for Indigenous Peoples

2023· article· en· W4313580030 on OpenAlexaffabout
Ashley Wilkinson, Rebecca Schiff, Jacquie Kidd, Helle Møller

Bibliographic record

VenueInternational Journal of Critical Indigenous Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsLakehead UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsCultural safetyAotearoaIndigenousRacismHealth careHealth equityInstitutional racismMetisTreaty of WaitangiAccountabilitySociologyColonialismPublic relationsPolitical scienceMedicineGender studies

Abstract

fetched live from OpenAlex

Indigenous peoples worldwide continue to face health inequities compared to non-Indigenous populations. Frameworks like cultural safety can be used to mitigate these inequities; however, this is not widely implemented in healthcare settings. Thus, additional research into barriers to providing culturally safe care are critical. To address this need, we examined the existing barriers to culturally safe care for Indigenous peoples in Canada, and Māori in Aotearoa New Zealand, through the perspectives of key informants. Major issues identified by key informants included systemic racism, lack of organisational accountability and/or buy-in, ineffective health-provider education, funding, health system structure, undervaluing Indigenous knowledge, negative framing, terminology, and changes to the concept of cultural safety over time. When examined closely, systemic racism and ongoing settler colonialism are the key driving forces underpinning many of the barriers identified. Findings from this research point to barriers at every level and require a system-wide, intersectoral approach in order to provide culturally safe care for Indigenous peoples and advance Indigenous health equity.

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)0.002
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science 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 score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.448
Teacher spread0.392 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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