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Record W3209294207 · doi:10.18584/iipj.2021.12.3.11058

Community Journey of Change Through Relational Determinants of Health

2021· article· en· W3209294207 on OpenAlexafffundvenueabout
Shelley Cardinal, Debra Pepler

Bibliographic record

VenueInternational Indigenous Policy Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsYork UniversityCanadian Red Cross Society
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousThrivingCeremonyCommunity healthSociologyPublic relationsPolitical scienceSocial sciencePublic healthGeographyMedicineEcologyNursing

Abstract

fetched live from OpenAlex

This article describes a model that maps Indigenous communities’ journeys from the cycle of violence arising from colonization to the circle of wellness through relational determinants of health. This model emerged from learning with Indigenous communities participating in research on violence prevention programming with the Canadian Red Cross. Indigenous communities have shown us that they are returning to a place of thriving by restoring relationality with land, culture, ceremony, and language. Therefore, the relational determinants of health comprise the foundational relationships that contribute to wellness. The Community Journey of Change model represents actions that communities can undertake in moving to wellness. The model has implications for policies, programs, and services for Indigenous communities as they begin to restore 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 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)0.006
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0110.027
Scholarly communication0.0100.009
Open science0.0020.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.509
GPT teacher head0.560
Teacher spread0.051 · 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

Citations4
Published2021
Admission routes4
Has abstractyes

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