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Record W3194542576 · doi:10.18584/iipj.2021.12.3.8204

San’yas Indigenous Cultural Safety Training as an Educational Intervention: Promoting Anti-Racism and Equity in Health Systems, Policies, and Practices

2021· article· en· W3194542576 on OpenAlexaffvenueabout
Annette J. Browne, Colleen Varcoe, Cheryl Ward

Bibliographic record

VenueInternational Indigenous Policy Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsProvincial Health Services AuthorityUniversity of British Columbia
Fundersnot available
KeywordsIndigenousRacismTransformational leadershipEquity (law)Health equityPsychological interventionSociologyDisadvantagedCultural safetyScope (computer science)Political sciencePublic relationsEconomic growthMedicinePublic healthNursingGender studiesLaw

Abstract

fetched live from OpenAlex

The San’yas Indigenous Cultural Safety Training Program is an Indigenous-led, policy-driven, and systems-level educational intervention to foster health equity and mitigate the effects of systemic racism experienced by Indigenous people in health and other sectors. Currently, San’yas is being scaled-up across Canada. This article focuses on the following: (a) the pedagogical underpinnings of San’yas grounded in transformational learning principles and Indigenous knowledges; (b) the scope, reach, and scale-up of San’yas as an explicit anti-racism educational intervention; (c) its unique program delivery approaches; and (d) program evaluation trends. We discuss the insights gained from implementing San’yas over the past decade, which will be relevant for leaders and policy-makers concerned with implementing anti-racism educational interventions as part of broader system transformation.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.483
Teacher spread0.404 · 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

Citations61
Published2021
Admission routes3
Has abstractyes

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