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Record W4413127812 · doi:10.1177/11771801251356067

Indigenous Tools for Living: decolonizing genocide-informed health education

2025· article· en· W4413127812 on OpenAlexaff
A. H. Young, Tonya Gomes, Belinda Lacombe, Stephanie Danielle Tipple

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsGenocideIndigenousDecolonizationSociologyPolitical scienceAnthropologyLawEcology

Abstract

fetched live from OpenAlex

Indigenous Tools for Living is an innovative health education program that supports and empowers frontline workers in dealing with complex trauma in culturally distinct and decolonizing ways. Built upon the foundational teachings of Indigenous Focusing-Oriented Therapy, Indigenous Tools for Living embodies a commitment to the health and wellness of Indigenous communities by prioritizing Indigenous knowledges and enriching our collective understanding of health education and leadership. Indigenous Tools for Living is facilitated by the Indigenous Focusing-Oriented Therapy Teaching Collective and clinically supervised by Shirley Turcotte, the grandmother of Indigenous Focusing-Oriented Therapy. Central to Indigenous Tools for Living is its commitment to cultural humility, reciprocity, and survivance. Through a holistic, culturally neutral framework that integrates culture, land, orality, community, and ethics, the program offers a transformative vision for Indigenous public health rooted in healing, justice, and self-determination; highlighting the importance of Indigenous-led initiatives in addressing historical trauma and advancing Indigenous health and wellness.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.319
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.059
GPT teacher head0.440
Teacher spread0.381 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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