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Record W4296078687 · doi:10.12927/hcq.2022.26894

Developing Relationships on a Shared Path to Reconciliation: The Core of Health Transformation and Safe Care for Indigenous People

2022· article· en· W4296078687 on OpenAlexvenueaboutno aff
Marion Maar, Ed Connors, Carol Fancott, William Mussell, Despina Papadopoulos

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGeneral partnershipEnablingPublic relationsPromotion (chess)Best practiceSpace (punctuation)SociologyNursingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This article describes the experience of a pan-Canadian health organization that led a quality improvement collaborative focused on suicide prevention and life promotion with Indigenous communities in northern and remote regions of Canada. Working in partnership with a Guidance Group, it became clear that working in a relational way that is culturally safe and acknowledges "two-eyed seeing" helps to create an ethical space in which open dialogue and collaboration can occur. Relational work enabled the improvement teams in the Promoting Life Together Collaborative to co-develop life promotion activities within their communities. The primacy of building relationships is at the core of reconciliation with Indigenous peoples and is a key enabler of system transformation required to support the health and wellness of Indigenous communities across Canada.

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)0.022
metaresearch head score (Gemma)0.020
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: none
Teacher disagreement score0.323
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0450.056
Scholarly communication0.0140.008
Open science0.0030.029
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.175
GPT teacher head0.421
Teacher spread0.246 · 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
Published2022
Admission routes2
Has abstractyes

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