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Record W2902720509 · doi:10.1177/0840470418803379

Truth and reconciliation: Healthcare organizational leadership

2018· article· en· W2902720509 on OpenAlexaffabout
Elizabeth McGibbon

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsMetisHealth carePrivilege (computing)IndigenousPublic relationsBlueprintWhite privilegeSociologyCall to actionPolitical sciencePower (physics)Corporate governanceRacismLawManagement

Abstract

fetched live from OpenAlex

Health leaders in organizational governance have a key role in enacting the Truth and Reconciliation Commission's Calls to Action. This discussion highlights historical and contemporary truths that can underpin action for addressing colonial impacts on Indigenous (First Nations, Metis, and Inuit) health outcomes and healthcare. Emphasis is on white settler roles and responsibilities, where health-related Calls provide a blueprint for health reconciliation leadership. There is broad agreement of the necessity to acknowledge and address key cornerstones of decolonization at individual, intermediary, and organizational stages: racism, white settler power and privilege, and cultural safety. Already existing leadership roles, responsibilities, and inter-organizational networks can form a solid foundation for health leaders to bring the Calls to the table-alongside First Nations, Metis, and Inuit peoples, in meetings, forums, and conferences and in lobbying efforts to influence the structural, systemic shape, and direction of healthcare in 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)0.065
metaresearch head score (Gemma)0.059
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 score0.987
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.059
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0260.056
Scholarly communication0.0350.021
Open science0.0030.020
Research integrity0.0110.020
Insufficient payload (model declined to judge)0.0080.001

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 head0.098
GPT teacher head0.350
Teacher spread0.252 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

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