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Record W2903166612 · doi:10.1177/0840470418807948

Modelling change and cultural safety: A case study in northern British Columbia health system transformation

2018· article· en· W2903166612 on OpenAlexaboutno aff
Margo Greenwood

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousVisionSituatedCultural safetyPoliticsSociologyConceptual frameworkHealth carePublic relationsPolitical scienceSocial scienceLawAnthropology

Abstract

fetched live from OpenAlex

The relationship that Indigenous Peoples have to the Canadian healthcare system makes the system's weaknesses and complexities obvious. The long-standing lack of consideration to the historical and contemporary realities of Indigenous Peoples has resulted in miscommunication, misunderstanding, mistrust and racism. Health leaders, including health authorities, across the province are thus challenged to ensure that culturally safe environments are available and culturally safe practices are being used. This article begins with an overview of contemporary social political contexts in which Indigenous individual and collective realities are situated. Following is a conceptual discussion focused on health system change and the experiences of Indigenous Peoples. Change at structural, systemic and individual levels is the focus of the change model presented in this article. Throughout this exploration, examples of concrete actions currently underway in a health authority are offered. The article concludes with visions for future change.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.943
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0210.009
Scholarly communication0.0070.002
Open science0.0040.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.114
GPT teacher head0.418
Teacher spread0.304 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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